Source code for transformers.modeling_dpr

# coding=utf-8
# Copyright 2018 DPR Authors
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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" PyTorch DPR model for Open Domain Question Answering."""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
from torch import Tensor, nn

from .configuration_dpr import DPRConfig
from .file_utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_callable, replace_return_docstrings
from .modeling_bert import BertModel
from .modeling_outputs import BaseModelOutputWithPooling
from .modeling_utils import PreTrainedModel
from .utils import logging


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "DPRConfig"

DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/dpr-ctx_encoder-single-nq-base",
]
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/dpr-question_encoder-single-nq-base",
]
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/dpr-reader-single-nq-base",
]


##########
# Outputs
##########


[docs]@dataclass class DPRContextEncoderOutput(ModelOutput): """ Class for outputs of :class:`~transformers.DPRQuestionEncoder`. Args: pooler_output: (:obj:``torch.FloatTensor`` of shape ``(batch_size, embeddings_size)``): The DPR encoder outputs the `pooler_output` that corresponds to the context representation. Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer. This output is to be used to embed contexts for nearest neighbors queries with questions embeddings. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ pooler_output: torch.FloatTensor hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None
[docs]@dataclass class DPRQuestionEncoderOutput(ModelOutput): """ Class for outputs of :class:`~transformers.DPRQuestionEncoder`. Args: pooler_output: (:obj:``torch.FloatTensor`` of shape ``(batch_size, embeddings_size)``): The DPR encoder outputs the `pooler_output` that corresponds to the question representation. Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer. This output is to be used to embed questions for nearest neighbors queries with context embeddings. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ pooler_output: torch.FloatTensor hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None
[docs]@dataclass class DPRReaderOutput(ModelOutput): """ Class for outputs of :class:`~transformers.DPRQuestionEncoder`. Args: start_logits: (:obj:``torch.FloatTensor`` of shape ``(n_passages, sequence_length)``): Logits of the start index of the span for each passage. end_logits: (:obj:``torch.FloatTensor`` of shape ``(n_passages, sequence_length)``): Logits of the end index of the span for each passage. relevance_logits: (:obj:`torch.FloatTensor`` of shape ``(n_passages, )``): Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage to answer the question, compared to all the other passages. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ start_logits: torch.FloatTensor end_logits: torch.FloatTensor = None relevance_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None
class DPREncoder(PreTrainedModel): base_model_prefix = "bert_model" def __init__(self, config: DPRConfig): super().__init__(config) self.bert_model = BertModel(config) assert self.bert_model.config.hidden_size > 0, "Encoder hidden_size can't be zero" self.projection_dim = config.projection_dim if self.projection_dim > 0: self.encode_proj = nn.Linear(self.bert_model.config.hidden_size, config.projection_dim) self.init_weights() def forward( self, input_ids: Tensor, attention_mask: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = False, ) -> Union[BaseModelOutputWithPooling, Tuple[Tensor, ...]]: outputs = self.bert_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output, pooled_output = outputs[:2] pooled_output = sequence_output[:, 0, :] if self.projection_dim > 0: pooled_output = self.encode_proj(pooled_output) if not return_dict: return (sequence_output, pooled_output) + outputs[2:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @property def embeddings_size(self) -> int: if self.projection_dim > 0: return self.encode_proj.out_features return self.bert_model.config.hidden_size def init_weights(self): self.bert_model.init_weights() if self.projection_dim > 0: self.encode_proj.apply(self.bert_model._init_weights) class DPRSpanPredictor(PreTrainedModel): base_model_prefix = "encoder" def __init__(self, config: DPRConfig): super().__init__(config) self.encoder = DPREncoder(config) self.qa_outputs = nn.Linear(self.encoder.embeddings_size, 2) self.qa_classifier = nn.Linear(self.encoder.embeddings_size, 1) self.init_weights() def forward( self, input_ids: Tensor, attention_mask: Tensor, inputs_embeds: Optional[Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = False, ) -> Union[DPRReaderOutput, Tuple[Tensor, ...]]: # notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length n_passages, sequence_length = input_ids.size() if input_ids is not None else inputs_embeds.size()[:2] # feed encoder outputs = self.encoder( input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # compute logits logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) relevance_logits = self.qa_classifier(sequence_output[:, 0, :]) # resize start_logits = start_logits.view(n_passages, sequence_length) end_logits = end_logits.view(n_passages, sequence_length) relevance_logits = relevance_logits.view(n_passages) if not return_dict: return (start_logits, end_logits, relevance_logits) + outputs[2:] return DPRReaderOutput( start_logits=start_logits, end_logits=end_logits, relevance_logits=relevance_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def init_weights(self): self.encoder.init_weights() ################## # PreTrainedModel ################## class DPRPretrainedContextEncoder(PreTrainedModel): """An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DPRConfig load_tf_weights = None base_model_prefix = "ctx_encoder" def init_weights(self): self.ctx_encoder.init_weights() class DPRPretrainedQuestionEncoder(PreTrainedModel): """An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DPRConfig load_tf_weights = None base_model_prefix = "question_encoder" def init_weights(self): self.question_encoder.init_weights() class DPRPretrainedReader(PreTrainedModel): """An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DPRConfig load_tf_weights = None base_model_prefix = "span_predictor" def init_weights(self): self.span_predictor.encoder.init_weights() self.span_predictor.qa_classifier.apply(self.span_predictor.encoder.bert_model._init_weights) self.span_predictor.qa_outputs.apply(self.span_predictor.encoder.bert_model._init_weights) ############### # Actual Models ############### DPR_START_DOCSTRING = r""" This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config (:class:`~transformers.DPRConfig`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. """ DPR_ENCODERS_INPUTS_DOCSTRING = r""" Args: input_ids: (:obj:``torch.LongTensor`` of shape ``(batch_size, sequence_length)``): Indices of input sequence tokens in the vocabulary. To match pre-training, DPR input sequence should be formatted with [CLS] and [SEP] tokens as follows: (a) For sequence pairs (for a pair title+text for example): ``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]`` ``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1`` (b) For single sequences (for a question for example): ``tokens: [CLS] the dog is hairy . [SEP]`` ``token_type_ids: 0 0 0 0 0 0 0`` DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. Indices can be obtained using :class:`transformers.DPRTokenizer`. See :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. attention_mask: (:obj:``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. token_type_ids: (:obj:``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`): Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` corresponds to a `sentence B` token inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors output_attentions (:obj:`bool`, `optional`): If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): If set to ``True``, the hidden states tensors of all layers are returned. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. """ DPR_READER_INPUTS_DOCSTRING = r""" Args: input_ids: (:obj:``torch.LongTensor`` of shape ``(n_passages, sequence_length)``): Indices of input sequence tokens in the vocabulary. It has to be a sequence triplet with 1) the question and 2) the passages titles and 3) the passages texts To match pre-training, DPR `input_ids` sequence should be formatted with [CLS] and [SEP] with the format: [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids> DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. Indices can be obtained using :class:`transformers.DPRReaderTokenizer`. See :class:`transformers.DPRReaderTokenizer` for more details attention_mask: (:obj:torch.FloatTensor``, of shape ``(n_passages, sequence_length)``, `optional`: Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(n_passages, sequence_length, hidden_size)`, `optional`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors output_attentions (:obj:`bool`, `optional`): If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): If set to ``True``, the hidden states tensors of all layers are returned. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. """
[docs]@add_start_docstrings( "The bare DPRContextEncoder transformer outputting pooler outputs as context representations.", DPR_START_DOCSTRING, ) class DPRContextEncoder(DPRPretrainedContextEncoder): def __init__(self, config: DPRConfig): super().__init__(config) self.config = config self.ctx_encoder = DPREncoder(config) self.init_weights()
[docs] @add_start_docstrings_to_callable(DPR_ENCODERS_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DPRContextEncoderOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_attentions=None, output_hidden_states=None, return_dict=None, ) -> Union[DPRContextEncoderOutput, Tuple[Tensor, ...]]: r""" Return: Examples:: >>> from transformers import DPRContextEncoder, DPRContextEncoderTokenizer >>> tokenizer = DPRContextEncoderTokenizer.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base') >>> model = DPRContextEncoder.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', return_dict=True) >>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"] >>> embeddings = model(input_ids).pooler_output """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = ( torch.ones(input_shape, device=device) if input_ids is None else (input_ids != self.config.pad_token_id) ) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) outputs = self.ctx_encoder( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs[1:] return DPRContextEncoderOutput( pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions )
[docs]@add_start_docstrings( "The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.", DPR_START_DOCSTRING, ) class DPRQuestionEncoder(DPRPretrainedQuestionEncoder): def __init__(self, config: DPRConfig): super().__init__(config) self.config = config self.question_encoder = DPREncoder(config) self.init_weights()
[docs] @add_start_docstrings_to_callable(DPR_ENCODERS_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DPRQuestionEncoderOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_attentions=None, output_hidden_states=None, return_dict=None, ) -> Union[DPRQuestionEncoderOutput, Tuple[Tensor, ...]]: r""" Return: Examples:: >>> from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer >>> tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base') >>> model = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base', return_dict=True) >>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"] >>> embeddings = model(input_ids).pooler_output """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = ( torch.ones(input_shape, device=device) if input_ids is None else (input_ids != self.config.pad_token_id) ) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) outputs = self.question_encoder( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs[1:] return DPRQuestionEncoderOutput( pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions )
[docs]@add_start_docstrings( "The bare DPRReader transformer outputting span predictions.", DPR_START_DOCSTRING, ) class DPRReader(DPRPretrainedReader): def __init__(self, config: DPRConfig): super().__init__(config) self.config = config self.span_predictor = DPRSpanPredictor(config) self.init_weights()
[docs] @add_start_docstrings_to_callable(DPR_READER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DPRReaderOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_attentions: bool = None, output_hidden_states: bool = None, return_dict=None, ) -> Union[DPRReaderOutput, Tuple[Tensor, ...]]: r""" Return: Examples:: >>> from transformers import DPRReader, DPRReaderTokenizer >>> tokenizer = DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base') >>> model = DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base', return_dict=True) >>> encoded_inputs = tokenizer( ... questions=["What is love ?"], ... titles=["Haddaway"], ... texts=["'What Is Love' is a song recorded by the artist Haddaway"], ... return_tensors='pt' ... ) >>> outputs = model(**encoded_inputs) >>> start_logits = outputs.stat_logits >>> end_logits = outputs.end_logits >>> relevance_logits = outputs.relevance_logits """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) return self.span_predictor( input_ids, attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, )