Source code for transformers.modeling_retribert

# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# distributed under the License is distributed on an "AS IS" BASIS,
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RetriBERT model

import math

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint

from .configuration_retribert import RetriBertConfig
from .file_utils import add_start_docstrings
from .modeling_bert import BertModel
from .modeling_utils import PreTrainedModel
from .utils import logging

logger = logging.get_logger(__name__)

    # See all RetriBert models at

class RetriBertPreTrainedModel(PreTrainedModel):
    """An abstract class to handle weights initialization and
    a simple interface for downloading and loading pretrained models.

    config_class = RetriBertConfig
    load_tf_weights = None
    base_model_prefix = "retribert"

    def _init_weights(self, module):
        """ Initialize the weights """
        if isinstance(module, (nn.Linear, nn.Embedding)):
  , std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
        if isinstance(module, nn.Linear) and module.bias is not None:


    This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
    methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
    pruning heads etc.)

    This model is also a PyTorch `torch.nn.Module <>`__ subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
    usage and behavior.

        config (:class:`~transformers.RetriBertConfig`): 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.

[docs]@add_start_docstrings( """Bert Based model to embed queries or document for document retreival. """, RETRIBERT_START_DOCSTRING, ) class RetriBertModel(RetriBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.projection_dim = config.projection_dim self.bert_query = BertModel(config) self.bert_doc = None if config.share_encoders else BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.project_query = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.project_doc = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.ce_loss = nn.CrossEntropyLoss(reduction="mean") self.init_weights() def embed_sentences_checkpointed( self, input_ids, attention_mask, sent_encoder, checkpoint_batch_size=-1, ): # reproduces BERT forward pass with checkpointing if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: return sent_encoder(input_ids, attention_mask=attention_mask)[1] else: # prepare implicit variables device = input_ids.device input_shape = input_ids.size() token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) head_mask = [None] * sent_encoder.config.num_hidden_layers extended_attention_mask: torch.Tensor = sent_encoder.get_extended_attention_mask( attention_mask, input_shape, device ) # define function for cehckpointing def partial_encode(*inputs): encoder_outputs = sent_encoder.encoder( inputs[0], attention_mask=inputs[1], head_mask=head_mask, ) sequence_output = encoder_outputs[0] pooled_output = sent_encoder.pooler(sequence_output) return pooled_output # run embedding layer on everything at once embedding_output = sent_encoder.embeddings( input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None ) # run encoding and pooling on one mini-batch at a time pooled_output_list = [] for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) pooled_output_list.append(pooled_output) return, dim=0) def embed_questions( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): q_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query, checkpoint_batch_size, ) return self.project_query(q_reps) def embed_answers( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): a_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query if self.bert_doc is None else self.bert_doc, checkpoint_batch_size, ) return self.project_doc(a_reps)
[docs] def forward( self, input_ids_query, attention_mask_query, input_ids_doc, attention_mask_doc, checkpoint_batch_size=-1 ): r""" Args: input_ids_query (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the queries in a batch. Indices can be obtained using :class:`~transformers.RetriBertTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. `What are input IDs? <../glossary.html#input-ids>`__ attention_mask_query (:obj:`torch.FloatTensor` of shape :obj:`(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 tokens that are **maked**. `What are attention masks? <../glossary.html#attention-mask>`__ input_ids_doc (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the documents in a batch. attention_mask_doc (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on documents padding token indices. checkpoint_batch_size (:obj:`int`, `optional`, defaults to `:obj:`-1`): If greater than 0, uses gradient checkpointing to only compute sequence representation on :obj:`checkpoint_batch_size` examples at a time on the GPU. All query representations are still compared to all document representations in the batch. Return: :obj:`torch.FloatTensor`: The bidirectional cross-entropy loss obtained while trying to match each query to its corresponding document and each cocument to its corresponding query in the batch """ device = input_ids_query.device q_reps = self.embed_questions(input_ids_query, attention_mask_query, checkpoint_batch_size) a_reps = self.embed_answers(input_ids_doc, attention_mask_doc, checkpoint_batch_size) compare_scores =, a_reps.t()) loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) loss = (loss_qa + loss_aq) / 2 return loss