592 lines
22 KiB
Python
592 lines
22 KiB
Python
# coding=utf-8
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# Copyright 2018 DPR Authors, The Hugging Face Team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch DPR model for Open Domain Question Answering."""
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from dataclasses import dataclass
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from typing import Optional, Union
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import torch
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from torch import Tensor, nn
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from ...modeling_outputs import BaseModelOutputWithPooling
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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ModelOutput,
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auto_docstring,
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logging,
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)
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from ..bert.modeling_bert import BertModel
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from .configuration_dpr import DPRConfig
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logger = logging.get_logger(__name__)
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##########
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# Outputs
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##########
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@dataclass
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@auto_docstring(
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custom_intro="""
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Class for outputs of [`DPRQuestionEncoder`].
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"""
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)
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class DPRContextEncoderOutput(ModelOutput):
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r"""
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pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`):
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The DPR encoder outputs the *pooler_output* that corresponds to the context representation. Last layer
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hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
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This output is to be used to embed contexts for nearest neighbors queries with questions embeddings.
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"""
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pooler_output: torch.FloatTensor
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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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@dataclass
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@auto_docstring(
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custom_intro="""
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Class for outputs of [`DPRQuestionEncoder`].
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"""
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)
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class DPRQuestionEncoderOutput(ModelOutput):
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r"""
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pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`):
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The DPR encoder outputs the *pooler_output* that corresponds to the question representation. Last layer
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hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
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This output is to be used to embed questions for nearest neighbors queries with context embeddings.
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"""
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pooler_output: torch.FloatTensor
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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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@dataclass
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@auto_docstring(
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custom_intro="""
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Class for outputs of [`DPRQuestionEncoder`].
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"""
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)
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class DPRReaderOutput(ModelOutput):
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r"""
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start_logits (`torch.FloatTensor` of shape `(n_passages, sequence_length)`):
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Logits of the start index of the span for each passage.
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end_logits (`torch.FloatTensor` of shape `(n_passages, sequence_length)`):
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Logits of the end index of the span for each passage.
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relevance_logits (`torch.FloatTensor` of shape `(n_passages, )`):
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Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage to answer the
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question, compared to all the other passages.
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"""
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start_logits: torch.FloatTensor
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end_logits: Optional[torch.FloatTensor] = None
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relevance_logits: Optional[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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@auto_docstring
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class DPRPreTrainedModel(PreTrainedModel):
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_supports_sdpa = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, nn.Linear):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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class DPREncoder(DPRPreTrainedModel):
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base_model_prefix = "bert_model"
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def __init__(self, config: DPRConfig):
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super().__init__(config)
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self.bert_model = BertModel(config, add_pooling_layer=False)
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if self.bert_model.config.hidden_size <= 0:
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raise ValueError("Encoder hidden_size can't be zero")
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self.projection_dim = config.projection_dim
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if self.projection_dim > 0:
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self.encode_proj = nn.Linear(self.bert_model.config.hidden_size, config.projection_dim)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: Tensor,
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attention_mask: Optional[Tensor] = None,
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token_type_ids: Optional[Tensor] = None,
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inputs_embeds: Optional[Tensor] = None,
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output_attentions: bool = False,
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output_hidden_states: bool = False,
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return_dict: bool = False,
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) -> Union[BaseModelOutputWithPooling, tuple[Tensor, ...]]:
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outputs = self.bert_model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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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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pooled_output = sequence_output[:, 0, :]
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if self.projection_dim > 0:
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pooled_output = self.encode_proj(pooled_output)
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if not return_dict:
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return (sequence_output, pooled_output) + outputs[2:]
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return BaseModelOutputWithPooling(
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last_hidden_state=sequence_output,
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pooler_output=pooled_output,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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@property
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def embeddings_size(self) -> int:
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if self.projection_dim > 0:
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return self.encode_proj.out_features
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return self.bert_model.config.hidden_size
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class DPRSpanPredictor(DPRPreTrainedModel):
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base_model_prefix = "encoder"
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def __init__(self, config: DPRConfig):
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super().__init__(config)
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self.encoder = DPREncoder(config)
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self.qa_outputs = nn.Linear(self.encoder.embeddings_size, 2)
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self.qa_classifier = nn.Linear(self.encoder.embeddings_size, 1)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: Tensor,
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attention_mask: Tensor,
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inputs_embeds: Optional[Tensor] = None,
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output_attentions: bool = False,
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output_hidden_states: bool = False,
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return_dict: bool = False,
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) -> Union[DPRReaderOutput, tuple[Tensor, ...]]:
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# notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length
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n_passages, sequence_length = input_ids.size() if input_ids is not None else inputs_embeds.size()[:2]
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# feed encoder
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outputs = self.encoder(
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input_ids,
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attention_mask=attention_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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# compute logits
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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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relevance_logits = self.qa_classifier(sequence_output[:, 0, :])
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# resize
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start_logits = start_logits.view(n_passages, sequence_length)
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end_logits = end_logits.view(n_passages, sequence_length)
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relevance_logits = relevance_logits.view(n_passages)
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if not return_dict:
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return (start_logits, end_logits, relevance_logits) + outputs[2:]
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return DPRReaderOutput(
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start_logits=start_logits,
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end_logits=end_logits,
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relevance_logits=relevance_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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##################
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# PreTrainedModel
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##################
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class DPRPretrainedContextEncoder(DPRPreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config: DPRConfig
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load_tf_weights = None
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base_model_prefix = "ctx_encoder"
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class DPRPretrainedQuestionEncoder(DPRPreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config: DPRConfig
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load_tf_weights = None
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base_model_prefix = "question_encoder"
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class DPRPretrainedReader(DPRPreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config: DPRConfig
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load_tf_weights = None
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base_model_prefix = "span_predictor"
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###############
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# Actual Models
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###############
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@auto_docstring(
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custom_intro="""
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The bare DPRContextEncoder transformer outputting pooler outputs as context representations.
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"""
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)
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class DPRContextEncoder(DPRPretrainedContextEncoder):
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def __init__(self, config: DPRConfig):
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super().__init__(config)
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self.config = config
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self.ctx_encoder = DPREncoder(config)
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# Initialize weights and apply final processing
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self.post_init()
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@auto_docstring
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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attention_mask: Optional[Tensor] = None,
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token_type_ids: Optional[Tensor] = None,
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inputs_embeds: Optional[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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return_dict: Optional[bool] = None,
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) -> Union[DPRContextEncoderOutput, tuple[Tensor, ...]]:
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r"""
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
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formatted with [CLS] and [SEP] tokens as follows:
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(a) For sequence pairs (for a pair title+text for example):
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```
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tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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```
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(b) For single sequences (for a question for example):
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```
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tokens: [CLS] the dog is hairy . [SEP]
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token_type_ids: 0 0 0 0 0 0 0
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```
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DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
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rather than the left.
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Indices can be obtained using [`AutoTokenizer`]. 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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Examples:
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```python
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>>> from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
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>>> tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
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>>> model = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
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>>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"]
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>>> embeddings = model(input_ids).pooler_output
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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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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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = input_ids.size()
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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if attention_mask is None:
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attention_mask = (
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torch.ones(input_shape, device=device)
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if input_ids is None
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else (input_ids != self.config.pad_token_id)
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)
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if token_type_ids is None:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
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outputs = self.ctx_encoder(
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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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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if not return_dict:
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return outputs[1:]
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return DPRContextEncoderOutput(
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pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
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)
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@auto_docstring(
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custom_intro="""
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The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.
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"""
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)
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class DPRQuestionEncoder(DPRPretrainedQuestionEncoder):
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def __init__(self, config: DPRConfig):
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super().__init__(config)
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self.config = config
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self.question_encoder = DPREncoder(config)
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# Initialize weights and apply final processing
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self.post_init()
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@auto_docstring
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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attention_mask: Optional[Tensor] = None,
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token_type_ids: Optional[Tensor] = None,
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inputs_embeds: Optional[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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return_dict: Optional[bool] = None,
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) -> Union[DPRQuestionEncoderOutput, tuple[Tensor, ...]]:
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r"""
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
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formatted with [CLS] and [SEP] tokens as follows:
|
|
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(a) For sequence pairs (for a pair title+text for example):
|
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```
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tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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```
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(b) For single sequences (for a question for example):
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```
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tokens: [CLS] the dog is hairy . [SEP]
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token_type_ids: 0 0 0 0 0 0 0
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```
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|
DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
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rather than the left.
|
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|
|
Indices can be obtained using [`AutoTokenizer`]. 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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Examples:
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```python
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>>> from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
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>>> tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
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>>> model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
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>>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"]
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>>> embeddings = model(input_ids).pooler_output
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```
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"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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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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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
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input_shape = input_ids.size()
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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if attention_mask is None:
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attention_mask = (
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torch.ones(input_shape, device=device)
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if input_ids is None
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else (input_ids != self.config.pad_token_id)
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)
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if token_type_ids is None:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
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outputs = self.question_encoder(
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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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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if not return_dict:
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return outputs[1:]
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return DPRQuestionEncoderOutput(
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pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
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)
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@auto_docstring(
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custom_intro="""
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The bare DPRReader transformer outputting span predictions.
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"""
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)
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class DPRReader(DPRPretrainedReader):
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def __init__(self, config: DPRConfig):
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super().__init__(config)
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self.config = config
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self.span_predictor = DPRSpanPredictor(config)
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# Initialize weights and apply final processing
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self.post_init()
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@auto_docstring
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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attention_mask: Optional[Tensor] = None,
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|
inputs_embeds: Optional[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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return_dict: Optional[bool] = None,
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|
) -> Union[DPRReaderOutput, tuple[Tensor, ...]]:
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r"""
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|
input_ids (`tuple[torch.LongTensor]` of shapes `(n_passages, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. It has to be a sequence triplet with 1) the question
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|
and 2) the passages titles and 3) the passages texts To match pretraining, DPR `input_ids` sequence should
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|
be formatted with [CLS] and [SEP] with the format:
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|
|
|
`[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>`
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|
|
|
DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
|
|
rather than the left.
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|
|
|
Indices can be obtained using [`DPRReaderTokenizer`]. See this class documentation for more details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
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|
inputs_embeds (`torch.FloatTensor` of shape `(n_passages, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `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 than the
|
|
model's internal embedding lookup matrix.
|
|
|
|
Examples:
|
|
|
|
```python
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|
>>> from transformers import DPRReader, DPRReaderTokenizer
|
|
|
|
>>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
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|
>>> model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
|
|
>>> encoded_inputs = tokenizer(
|
|
... questions=["What is love ?"],
|
|
... titles=["Haddaway"],
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|
... texts=["'What Is Love' is a song recorded by the artist Haddaway"],
|
|
... return_tensors="pt",
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|
... )
|
|
>>> outputs = model(**encoded_inputs)
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|
>>> start_logits = outputs.start_logits
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|
>>> 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:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
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,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"DPRContextEncoder",
|
|
"DPRPretrainedContextEncoder",
|
|
"DPRPreTrainedModel",
|
|
"DPRPretrainedQuestionEncoder",
|
|
"DPRPretrainedReader",
|
|
"DPRQuestionEncoder",
|
|
"DPRReader",
|
|
]
|