models/sequence_labeling/lebert.py

# from transformers.configuration_bert import BertConfig
# from transformers import BertPreTrainedModel
# from transformers.modeling_bert import BertEmbeddings, BertEncoder, BertPooler, BertLayer, BaseModelOutput, BaseModelOutputWithPooling
# from transformers.modeling_bert import BERT_INPUTS_DOCSTRING, _TOKENIZER_FOR_DOC, _CONFIG_FOR_DOC

from transformers.models.bert.modeling_bert import BertConfig, BertPreTrainedModel, BertEmbeddings, \
    BertPooler, BertLayer, BaseModelOutputWithPoolingAndCrossAttentions, BaseModelOutputWithPastAndCrossAttentions
from transformers.models.bert.modeling_bert import BERT_INPUTS_DOCSTRING, _TOKENIZER_FOR_DOC, _CONFIG_FOR_DOC
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import (
    add_code_sample_docstrings,
    add_start_docstrings_to_model_forward,
)

class WordEmbeddingAdapter(nn.Module):

    def __init__(self, config):
        super(WordEmbeddingAdapter, self).__init__()
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.tanh = nn.Tanh()

        self.linear1 = nn.Linear(config.word_embed_dim, config.hidden_size)
        self.linear2 = nn.Linear(config.hidden_size, config.hidden_size)

        attn_W = torch.zeros(config.hidden_size, config.hidden_size)
        self.attn_W = nn.Parameter(attn_W)
        self.attn_W.data.normal_(mean=0.0, std=config.initializer_range)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, layer_output, word_embeddings, word_mask):
        """
        :param layer_output:bert layer的输出,[b_size, len_input, d_model]
        :param word_embeddings:每个汉字对应的词向量集合,[b_size, len_input, num_word, d_word]
        :param word_mask:每个汉字对应的词向量集合的attention mask, [b_size, len_input, num_word]
        """

        # transform
        # 将词向量，与字符向量进行维度对齐
        word_outputs = self.linear1(word_embeddings)
        word_outputs = self.tanh(word_outputs)
        word_outputs = self.linear2(word_outputs)
        word_outputs = self.dropout(word_outputs)   # word_outputs：[b_size, len_input, num_word, d_model]
        # if type(word_mask) == torch.long:
        word_mask = word_mask.bool()

        # 计算每个字符向量，与其对应的所有词向量的注意力权重，然后加权求和。采用双线性映射计算注意力权重
        # layer_output = layer_output.unsqueeze(2)    # layer_output：[b_size, len_input, 1, d_model]
        socres = torch.matmul(layer_output.unsqueeze(2), self.attn_W)  # [b_size, len_input, 1, d_model]
        socres = torch.matmul(socres, torch.transpose(word_outputs, 2, 3))  # [b_size, len_input, 1, num_word]
        socres = socres.squeeze(2)  # [b_size, len_input, num_word]
        socres.masked_fill_(word_mask, -1e9)  # 将pad的注意力设为很小的数
        socres = F.softmax(socres, dim=-1)  # [b_size, len_input, num_word]
        attn = socres.unsqueeze(-1)  # [b_size, len_input, num_word, 1]

        weighted_word_embedding = torch.sum(word_outputs * attn, dim=2)  # [N, L, D]   # 加权求和，得到每个汉字对应的词向量集合的表示
        layer_output = layer_output + weighted_word_embedding

        layer_output = self.dropout(layer_output)
        layer_output = self.layer_norm(layer_output)

        return layer_output


class LEBertModel(BertPreTrainedModel):
    """

    The model can behave as an encoder (with only self-attention) as well
    as a decoder, in which case a layer of cross-attention is added between
    the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani,
    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

    To behave as an decoder the model needs to be initialized with the
    :obj:`is_decoder` argument of the configuration set to :obj:`True`.
    To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
    argument and :obj:`add_cross_attention` set to :obj:`True`; an
    :obj:`encoder_hidden_states` is then expected as an input to the forward pass.

    .. _`Attention is all you need`:
        https://arxiv.org/abs/1706.03762

    """

    def __init__(self, config):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)
        self.encoder = BertEncoder(config)
        self.pooler = BertPooler(config)

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """Prunes heads of the model.
        heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
        See base class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint="bert-base-uncased",
        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        word_embeddings=None,
        word_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
            if the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask
            is used in the cross-attention if the model is configured as a decoder.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        """
        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 token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(
            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
        )
        encoder_outputs = self.encoder(
            embedding_output,
            word_embeddings=word_embeddings,
            word_mask=word_mask,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output)

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class BertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
        self.word_embedding_adapter = WordEmbeddingAdapter(config)

    def forward(
        self,
        hidden_states,
        word_embeddings,
        word_mask,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=False,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        next_decoder_cache = () if use_cache else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None

            if getattr(self.config, "gradient_checkpointing", False):

                if use_cache:
                    # logger.warning(
                    #     "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    # )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                )
            hidden_states = layer_outputs[0]
            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

            # 在第i层之后，进行融合
            # if i == self.config.add_layer:
            if i >= int(self.config.add_layer): # edit by wjn
                hidden_states = self.word_embedding_adapter(hidden_states, word_embeddings, word_mask)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        # if not return_dict:
        #     return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    next_decoder_cache,
                    all_hidden_states,
                    all_attentions,
                    # all_cross_attentions,
                ]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            past_key_values=next_decoder_cache,
        )