fastNLP/fastNLP/modules/encoder/_bert.py

"""
这个页面的代码很大程度上参考了https://github.com/huggingface/pytorch-pretrained-BERT的代码
"""



from ...core.vocabulary import Vocabulary
import collections

import unicodedata
from ...io.file_utils import _get_base_url, cached_path
import numpy as np
from itertools import chain
import copy
import json
import math
import os

import torch
from torch import nn

CONFIG_FILE = 'bert_config.json'
MODEL_WEIGHTS = 'pytorch_model.bin'


def gelu(x):
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


def swish(x):
    return x * torch.sigmoid(x)


ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}


class BertLayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-12):
        super(BertLayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x + self.bias


class BertEmbeddings(nn.Module):
    def __init__(self, vocab_size, hidden_size, max_position_embeddings, type_vocab_size, hidden_dropout_prob):
        super(BertEmbeddings, self).__init__()
        self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
        self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size)
        self.token_type_embeddings = nn.Embedding(type_vocab_size, hidden_size)

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = BertLayerNorm(hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, input_ids, token_type_ids=None):
        seq_length = input_ids.size(1)
        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)

        words_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = words_embeddings + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class BertSelfAttention(nn.Module):
    def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob):
        super(BertSelfAttention, self).__init__()
        if hidden_size % num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (hidden_size, num_attention_heads))
        self.num_attention_heads = num_attention_heads
        self.attention_head_size = int(hidden_size / num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(hidden_size, self.all_head_size)
        self.key = nn.Linear(hidden_size, self.all_head_size)
        self.value = nn.Linear(hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, hidden_states, attention_mask):
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
        attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        return context_layer


class BertSelfOutput(nn.Module):
    def __init__(self, hidden_size, hidden_dropout_prob):
        super(BertSelfOutput, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.LayerNorm = BertLayerNorm(hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertAttention(nn.Module):
    def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):
        super(BertAttention, self).__init__()
        self.self = BertSelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob)
        self.output = BertSelfOutput(hidden_size, hidden_dropout_prob)

    def forward(self, input_tensor, attention_mask):
        self_output = self.self(input_tensor, attention_mask)
        attention_output = self.output(self_output, input_tensor)
        return attention_output


class BertIntermediate(nn.Module):
    def __init__(self, hidden_size, intermediate_size, hidden_act):
        super(BertIntermediate, self).__init__()
        self.dense = nn.Linear(hidden_size, intermediate_size)
        self.intermediate_act_fn = ACT2FN[hidden_act] \
            if isinstance(hidden_act, str) else hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class BertOutput(nn.Module):
    def __init__(self, hidden_size, intermediate_size, hidden_dropout_prob):
        super(BertOutput, self).__init__()
        self.dense = nn.Linear(intermediate_size, hidden_size)
        self.LayerNorm = BertLayerNorm(hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertLayer(nn.Module):
    def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob,
                 intermediate_size, hidden_act):
        super(BertLayer, self).__init__()
        self.attention = BertAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob,
                                       hidden_dropout_prob)
        self.intermediate = BertIntermediate(hidden_size, intermediate_size, hidden_act)
        self.output = BertOutput(hidden_size, intermediate_size, hidden_dropout_prob)

    def forward(self, hidden_states, attention_mask):
        attention_output = self.attention(hidden_states, attention_mask)
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


class BertEncoder(nn.Module):
    def __init__(self, num_hidden_layers, hidden_size, num_attention_heads, attention_probs_dropout_prob,
                 hidden_dropout_prob,
                 intermediate_size, hidden_act):
        super(BertEncoder, self).__init__()
        layer = BertLayer(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob,
                          intermediate_size, hidden_act)
        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_hidden_layers)])

    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
        all_encoder_layers = []
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states, attention_mask)
            if output_all_encoded_layers:
                all_encoder_layers.append(hidden_states)
        if not output_all_encoded_layers:
            all_encoder_layers.append(hidden_states)
        return all_encoder_layers


class BertPooler(nn.Module):
    def __init__(self, hidden_size):
        super(BertPooler, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class BertModel(nn.Module):
    """BERT(Bidirectional Embedding Representations from Transformers).

    如果你想使用预训练好的权重矩阵，请在以下网址下载.
    sources::

    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",


    用预训练权重矩阵来建立BERT模型::

        model = BertModel.from_pretrained("path/to/weights/directory")

    用随机初始化权重矩阵来建立BERT模型::

        model = BertModel()

    :param int vocab_size: 词表大小，默认值为30522，为BERT English uncase版本的词表大小
    :param int hidden_size: 隐层大小，默认值为768，为BERT base的版本
    :param int num_hidden_layers: 隐藏层数，默认值为12，为BERT base的版本
    :param int num_attention_heads: 多头注意力头数，默认值为12，为BERT base的版本
    :param int intermediate_size: FFN隐藏层大小，默认值是3072，为BERT base的版本
    :param str hidden_act: FFN隐藏层激活函数，默认值为``gelu``
    :param float hidden_dropout_prob: FFN隐藏层dropout，默认值为0.1
    :param float attention_probs_dropout_prob: Attention层的dropout，默认值为0.1
    :param int max_position_embeddings: 最大的序列长度，默认值为512，
    :param int type_vocab_size: 最大segment数量，默认值为2
    :param int initializer_range: 初始化权重范围，默认值为0.02
    """

    def __init__(self, vocab_size=30522,
                 hidden_size=768,
                 num_hidden_layers=12,
                 num_attention_heads=12,
                 intermediate_size=3072,
                 hidden_act="gelu",
                 hidden_dropout_prob=0.1,
                 attention_probs_dropout_prob=0.1,
                 max_position_embeddings=512,
                 type_vocab_size=2,
                 initializer_range=0.02):
        super(BertModel, self).__init__()
        self.hidden_size = hidden_size
        self.embeddings = BertEmbeddings(vocab_size, hidden_size, max_position_embeddings,
                                         type_vocab_size, hidden_dropout_prob)
        self.encoder = BertEncoder(num_hidden_layers, hidden_size, num_attention_heads,
                                   attention_probs_dropout_prob, hidden_dropout_prob, intermediate_size,
                                   hidden_act)
        self.pooler = BertPooler(hidden_size)
        self.initializer_range = initializer_range

        self.apply(self.init_bert_weights)

    def init_bert_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.initializer_range)
        elif isinstance(module, BertLayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()

    def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True):
        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids)
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)

        # We create a 3D attention mask from a 2D tensor mask.
        # Sizes are [batch_size, 1, 1, to_seq_length]
        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
        # this attention mask is more simple than the triangular masking of causal attention
        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0

        embedding_output = self.embeddings(input_ids, token_type_ids)
        encoded_layers = self.encoder(embedding_output,
                                      extended_attention_mask,
                                      output_all_encoded_layers=output_all_encoded_layers)
        sequence_output = encoded_layers[-1]
        pooled_output = self.pooler(sequence_output)
        if not output_all_encoded_layers:
            encoded_layers = encoded_layers[-1]
        return encoded_layers, pooled_output

    @classmethod
    def from_pretrained(cls, pretrained_model_dir, state_dict=None, *inputs, **kwargs):
        # Load config
        config_file = os.path.join(pretrained_model_dir, CONFIG_FILE)
        config = json.load(open(config_file, "r"))
        # config = BertConfig.from_json_file(config_file)
        # logger.info("Model config {}".format(config))
        # Instantiate model.
        model = cls(*inputs, **config, **kwargs)
        if state_dict is None:
            weights_path = os.path.join(pretrained_model_dir, MODEL_WEIGHTS)
            state_dict = torch.load(weights_path)

        old_keys = []
        new_keys = []
        for key in state_dict.keys():
            new_key = None
            if 'gamma' in key:
                new_key = key.replace('gamma', 'weight')
            if 'beta' in key:
                new_key = key.replace('beta', 'bias')
            if new_key:
                old_keys.append(key)
                new_keys.append(new_key)
        for old_key, new_key in zip(old_keys, new_keys):
            state_dict[new_key] = state_dict.pop(old_key)

        missing_keys = []
        unexpected_keys = []
        error_msgs = []
        # copy state_dict so _load_from_state_dict can modify it
        metadata = getattr(state_dict, '_metadata', None)
        state_dict = state_dict.copy()
        if metadata is not None:
            state_dict._metadata = metadata

        def load(module, prefix=''):
            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
            module._load_from_state_dict(
                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
            for name, child in module._modules.items():
                if child is not None:
                    load(child, prefix + name + '.')

        load(model, prefix='' if hasattr(model, 'bert') else 'bert.')
        if len(missing_keys) > 0:
            print("Weights of {} not initialized from pretrained model: {}".format(
                model.__class__.__name__, missing_keys))
        if len(unexpected_keys) > 0:
            print("Weights from pretrained model not used in {}: {}".format(
                model.__class__.__name__, unexpected_keys))
        return model












def whitespace_tokenize(text):
    """Runs basic whitespace cleaning and splitting on a piece of text."""
    text = text.strip()
    if not text:
        return []
    tokens = text.split()
    return tokens


class WordpieceTokenizer(object):
    """Runs WordPiece tokenization."""

    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
        self.vocab = vocab
        self.unk_token = unk_token
        self.max_input_chars_per_word = max_input_chars_per_word

    def tokenize(self, text):
        """Tokenizes a piece of text into its word pieces.

        This uses a greedy longest-match-first algorithm to perform tokenization
        using the given vocabulary.

        For example:
          input = "unaffable"
          output = ["un", "##aff", "##able"]

        Args:
          text: A single token or whitespace separated tokens. This should have
            already been passed through `BasicTokenizer`.

        Returns:
          A list of wordpiece tokens.
        """

        output_tokens = []
        for token in whitespace_tokenize(text):
            chars = list(token)
            if len(chars) > self.max_input_chars_per_word:
                output_tokens.append(self.unk_token)
                continue

            is_bad = False
            start = 0
            sub_tokens = []
            while start < len(chars):
                end = len(chars)
                cur_substr = None
                while start < end:
                    substr = "".join(chars[start:end])
                    if start > 0:
                        substr = "##" + substr
                    if substr in self.vocab:
                        cur_substr = substr
                        break
                    end -= 1
                if cur_substr is None:
                    is_bad = True
                    break
                sub_tokens.append(cur_substr)
                start = end

            if is_bad:
                output_tokens.append(self.unk_token)
            else:
                output_tokens.extend(sub_tokens)
        return output_tokens

def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    index = 0
    with open(vocab_file, "r", encoding="utf-8") as reader:
        while True:
            token = reader.readline()
            if not token:
                break
            token = token.strip()
            vocab[token] = index
            index += 1
    return vocab

class BasicTokenizer(object):
    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""

    def __init__(self,
                 do_lower_case=True,
                 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
        """Constructs a BasicTokenizer.

        Args:
          do_lower_case: Whether to lower case the input.
        """
        self.do_lower_case = do_lower_case
        self.never_split = never_split

    def tokenize(self, text):
        """Tokenizes a piece of text."""
        text = self._clean_text(text)
        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        text = self._tokenize_chinese_chars(text)
        orig_tokens = whitespace_tokenize(text)
        split_tokens = []
        for token in orig_tokens:
            if self.do_lower_case and token not in self.never_split:
                token = token.lower()
                token = self._run_strip_accents(token)
            split_tokens.extend(self._run_split_on_punc(token))

        output_tokens = whitespace_tokenize(" ".join(split_tokens))
        return output_tokens

    def _run_strip_accents(self, text):
        """Strips accents from a piece of text."""
        text = unicodedata.normalize("NFD", text)
        output = []
        for char in text:
            cat = unicodedata.category(char)
            if cat == "Mn":
                continue
            output.append(char)
        return "".join(output)

    def _run_split_on_punc(self, text):
        """Splits punctuation on a piece of text."""
        if text in self.never_split:
            return [text]
        chars = list(text)
        i = 0
        start_new_word = True
        output = []
        while i < len(chars):
            char = chars[i]
            if _is_punctuation(char):
                output.append([char])
                start_new_word = True
            else:
                if start_new_word:
                    output.append([])
                start_new_word = False
                output[-1].append(char)
            i += 1

        return ["".join(x) for x in output]

    def _tokenize_chinese_chars(self, text):
        """Adds whitespace around any CJK character."""
        output = []
        for char in text:
            cp = ord(char)
            if self._is_chinese_char(cp):
                output.append(" ")
                output.append(char)
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)

    def _is_chinese_char(self, cp):
        """Checks whether CP is the codepoint of a CJK character."""
        # This defines a "chinese character" as anything in the CJK Unicode block:
        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
        #
        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
        # despite its name. The modern Korean Hangul alphabet is a different block,
        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
        # space-separated words, so they are not treated specially and handled
        # like the all of the other languages.
        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
                (cp >= 0x3400 and cp <= 0x4DBF) or  #
                (cp >= 0x20000 and cp <= 0x2A6DF) or  #
                (cp >= 0x2A700 and cp <= 0x2B73F) or  #
                (cp >= 0x2B740 and cp <= 0x2B81F) or  #
                (cp >= 0x2B820 and cp <= 0x2CEAF) or
                (cp >= 0xF900 and cp <= 0xFAFF) or  #
                (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
            return True

        return False

    def _clean_text(self, text):
        """Performs invalid character removal and whitespace cleanup on text."""
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xfffd or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)

def _is_whitespace(char):
    """Checks whether `chars` is a whitespace character."""
    # \t, \n, and \r are technically contorl characters but we treat them
    # as whitespace since they are generally considered as such.
    if char == " " or char == "\t" or char == "\n" or char == "\r":
        return True
    cat = unicodedata.category(char)
    if cat == "Zs":
        return True
    return False


def _is_control(char):
    """Checks whether `chars` is a control character."""
    # These are technically control characters but we count them as whitespace
    # characters.
    if char == "\t" or char == "\n" or char == "\r":
        return False
    cat = unicodedata.category(char)
    if cat.startswith("C"):
        return True
    return False


def _is_punctuation(char):
    """Checks whether `chars` is a punctuation character."""
    cp = ord(char)
    # We treat all non-letter/number ASCII as punctuation.
    # Characters such as "^", "$", and "`" are not in the Unicode
    # Punctuation class but we treat them as punctuation anyways, for
    # consistency.
    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
        return True
    cat = unicodedata.category(char)
    if cat.startswith("P"):
        return True
    return False


class BertTokenizer(object):
    """Runs end-to-end tokenization: punctuation splitting + wordpiece"""

    def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
                 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
        """Constructs a BertTokenizer.

        Args:
          vocab_file: Path to a one-wordpiece-per-line vocabulary file
          do_lower_case: Whether to lower case the input
                         Only has an effect when do_wordpiece_only=False
          do_basic_tokenize: Whether to do basic tokenization before wordpiece.
          max_len: An artificial maximum length to truncate tokenized sequences to;
                         Effective maximum length is always the minimum of this
                         value (if specified) and the underlying BERT model's
                         sequence length.
          never_split: List of tokens which will never be split during tokenization.
                         Only has an effect when do_wordpiece_only=False
        """
        if not os.path.isfile(vocab_file):
            raise ValueError(
                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
        self.vocab = load_vocab(vocab_file)
        self.ids_to_tokens = collections.OrderedDict(
            [(ids, tok) for tok, ids in self.vocab.items()])
        self.do_basic_tokenize = do_basic_tokenize
        if do_basic_tokenize:
          self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
                                                never_split=never_split)
        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
        self.max_len = max_len if max_len is not None else int(1e12)

    def tokenize(self, text):
        split_tokens = []
        if self.do_basic_tokenize:
            for token in self.basic_tokenizer.tokenize(text):
                for sub_token in self.wordpiece_tokenizer.tokenize(token):
                    split_tokens.append(sub_token)
        else:
            split_tokens = self.wordpiece_tokenizer.tokenize(text)
        return split_tokens

    def convert_tokens_to_ids(self, tokens):
        """Converts a sequence of tokens into ids using the vocab."""
        ids = []
        for token in tokens:
            ids.append(self.vocab[token])
        if len(ids) > self.max_len:
            print(
                "Token indices sequence length is longer than the specified maximum "
                " sequence length for this BERT model ({} > {}). Running this"
                " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
            )
        return ids

    def convert_ids_to_tokens(self, ids):
        """Converts a sequence of ids in wordpiece tokens using the vocab."""
        tokens = []
        for i in ids:
            tokens.append(self.ids_to_tokens[i])
        return tokens

    def save_vocabulary(self, vocab_path):
        """Save the tokenizer vocabulary to a directory or file."""
        index = 0
        if os.path.isdir(vocab_path):
            vocab_file = os.path.join(vocab_path, VOCAB_NAME)
        with open(vocab_file, "w", encoding="utf-8") as writer:
            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    print("Saving vocabulary to {}: vocabulary indices are not consecutive."
                                   " Please check that the vocabulary is not corrupted!".format(vocab_file))
                    index = token_index
                writer.write(token + u'\n')
                index += 1
        return vocab_file

    @classmethod
    def from_pretrained(cls, model_dir, *inputs, **kwargs):
        """
        给定path，直接读取vocab.

        """
        pretrained_model_name_or_path = os.path.join(model_dir, VOCAB_NAME)
        print("loading vocabulary file {}".format(pretrained_model_name_or_path))
        max_len = 512
        kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
        # Instantiate tokenizer.
        tokenizer = cls(pretrained_model_name_or_path, *inputs, **kwargs)
        return tokenizer

VOCAB_NAME = 'vocab.txt'

class _WordBertModel(nn.Module):
    def __init__(self, model_dir:str, vocab:Vocabulary, layers:str='-1', pool_method:str='first', include_cls_sep:bool=False):
        super().__init__()

        self.tokenzier = BertTokenizer.from_pretrained(model_dir)
        self.encoder = BertModel.from_pretrained(model_dir)
        #  检查encoder_layer_number是否合理
        encoder_layer_number = len(self.encoder.encoder.layer)
        self.layers = list(map(int, layers.split(',')))
        for layer in self.layers:
            if layer<0:
                assert -layer<=encoder_layer_number, f"The layer index:{layer} is out of scope for " \
                    f"a bert model with {encoder_layer_number} layers."
            else:
                assert layer<encoder_layer_number, f"The layer index:{layer} is out of scope for " \
                    f"a bert model with {encoder_layer_number} layers."

        assert pool_method in ('avg', 'max', 'first', 'last')
        self.pool_method = pool_method

        self.include_cls_sep = include_cls_sep

        # 将所有vocab中word的wordpiece计算出来, 需要额外考虑[CLS]和[SEP]
        print("Start to generating word pieces for word.")
        word_to_wordpieces = []
        word_pieces_lengths = []
        for word, index in vocab:
            if index == vocab.padding_idx:  # pad是个特殊的符号
                word = '[PAD]'
            elif index == vocab.unknown_idx:
                word = '[UNK]'
            word_pieces = self.tokenzier.wordpiece_tokenizer.tokenize(word)
            word_pieces = self.tokenzier.convert_tokens_to_ids(word_pieces)
            word_to_wordpieces.append(word_pieces)
            word_pieces_lengths.append(len(word_pieces))
        self._cls_index = len(vocab)
        self._sep_index = len(vocab) + 1
        self._pad_index = vocab.padding_idx
        self._wordpiece_pad_index = self.tokenzier.convert_tokens_to_ids(['[PAD]'])[0]  # 需要用于生成word_piece
        word_to_wordpieces.append(self.tokenzier.convert_tokens_to_ids(['[CLS]']))
        word_to_wordpieces.append(self.tokenzier.convert_tokens_to_ids(['[SEP]']))
        self.word_to_wordpieces = np.array(word_to_wordpieces)
        self.word_pieces_lengths = nn.Parameter(torch.LongTensor(word_pieces_lengths), requires_grad=False)
        print("Successfully generate word pieces.")

    def forward(self, words):
        """

        :param words: torch.LongTensor, batch_size x max_len
        :return: num_layers x batch_size x max_len x hidden_size或者num_layers x batch_size x (max_len+2) x hidden_size
        """
        batch_size, max_word_len = words.size()
        seq_len = words.ne(self._pad_index).sum(dim=-1)
        batch_word_pieces_length = self.word_pieces_lengths[words]  # batch_size x max_len
        word_pieces_lengths = batch_word_pieces_length.sum(dim=-1)
        max_word_piece_length = word_pieces_lengths.max().item()
        # +2是由于需要加入[CLS]与[SEP]
        word_pieces = words.new_full((batch_size, max_word_piece_length+2), fill_value=self._wordpiece_pad_index)
        word_pieces[:, 0].fill_(self._cls_index)
        word_pieces[torch.arange(batch_size).to(words), word_pieces_lengths+1] = self._sep_index
        attn_masks = torch.zeros_like(word_pieces)
        # 1. 获取words的word_pieces的id，以及对应的span范围
        word_indexes = words.tolist()
        for i in range(batch_size):
            word_pieces_i = list(chain(*self.word_to_wordpieces[word_indexes[i]]))
            word_pieces[i, 1:len(word_pieces_i)+1] = torch.LongTensor(word_pieces_i)
            attn_masks[i, :len(word_pieces_i)+2].fill_(1)
        # 2. 获取hidden的结果，根据word_pieces进行对应的pool计算
        # all_outputs: [batch_size x max_len x hidden_size, batch_size x max_len x hidden_size, ...]
        bert_outputs, _ = self.encoder(word_pieces, token_type_ids=None, attention_mask=attn_masks,
                                           output_all_encoded_layers=True)
        # output_layers = [self.layers]  # len(self.layers) x batch_size x max_word_piece_length x hidden_size

        if self.include_cls_sep:
            outputs = bert_outputs[-1].new_zeros(len(self.layers), batch_size, max_word_len + 2,
                                                 bert_outputs[-1].size(-1))
            s_shift = 1
        else:
            outputs = bert_outputs[-1].new_zeros(len(self.layers), batch_size, max_word_len,
                                                 bert_outputs[-1].size(-1))
            s_shift = 0
        batch_word_pieces_cum_length = batch_word_pieces_length.new_zeros(batch_size, max_word_len + 1)
        batch_word_pieces_cum_length[:, 1:] = batch_word_pieces_length.cumsum(dim=-1)  # batch_size x max_len
        for l_index, l in enumerate(self.layers):
            output_layer = bert_outputs[l]
            # 从word_piece collapse到word的表示
            truncate_output_layer = output_layer[:, 1:-1]  # 删除[CLS]与[SEP] batch_size x len x hidden_size
            outputs_seq_len = seq_len + s_shift
            if self.pool_method == 'first':
                for i in range(batch_size):
                    i_word_pieces_cum_length = batch_word_pieces_cum_length[i, :seq_len[i]]  # 每个word的start位置
                    outputs[l_index, i, s_shift:outputs_seq_len[i]] = truncate_output_layer[i, i_word_pieces_cum_length]  # num_layer x batch_size x len x hidden_size
            elif self.pool_method == 'last':
                for i in range(batch_size):
                    i_word_pieces_cum_length = batch_word_pieces_cum_length[i, 1:seq_len[i]+1] - 1 # 每个word的end
                    outputs[l_index, i, s_shift:outputs_seq_len[i]] = truncate_output_layer[i, i_word_pieces_cum_length]
            elif self.pool_method == 'max':
                for i in range(batch_size):
                    for j in range(seq_len[i]):
                        start, end = batch_word_pieces_cum_length[i, j], batch_word_pieces_cum_length[i, j+1]
                        outputs[l_index, i, j+s_shift], _ = torch.max(truncate_output_layer[i, start:end], dim=-2)
            else:
                for i in range(batch_size):
                    for j in range(seq_len[i]):
                        start, end = batch_word_pieces_cum_length[i, j], batch_word_pieces_cum_length[i, j+1]
                        outputs[l_index, i, j+s_shift] = torch.mean(truncate_output_layer[i, start:end], dim=-2)
            if self.include_cls_sep:
                outputs[:, :, 0] = output_layer[:, 0]
                outputs[:, :, seq_len+s_shift] = output_layer[:, seq_len+s_shift]
        # 3. 最终的embedding结果
        return outputs


class _WordPieceBertModel(nn.Module):
    """
    这个模块用于直接计算word_piece的结果.

    """
    def __init__(self, model_dir:str, vocab:Vocabulary, layers:str='-1'):
        super().__init__()

        self.tokenzier = BertTokenizer.from_pretrained(model_dir)
        self.encoder = BertModel.from_pretrained(model_dir)
        #  检查encoder_layer_number是否合理
        encoder_layer_number = len(self.encoder.encoder.layer)
        self.layers = list(map(int, layers.split(',')))
        for layer in self.layers:
            if layer<0:
                assert -layer<=encoder_layer_number, f"The layer index:{layer} is out of scope for " \
                    f"a bert model with {encoder_layer_number} layers."
            else:
                assert layer<encoder_layer_number, f"The layer index:{layer} is out of scope for " \
                    f"a bert model with {encoder_layer_number} layers."

        # 将所有vocab中word的wordpiece计算出来, 需要额外考虑[CLS]和[SEP]
        print("Start to generating word pieces for word.")
        self.word_to_wordpieces = []
        self.word_pieces_length = []
        for word, index in vocab:
            if index == vocab.padding_idx:  # pad是个特殊的符号
                word = '[PAD]'
            elif index == vocab.unknown_idx:
                word = '[UNK]'
            word_pieces = self.tokenzier.wordpiece_tokenizer.tokenize(word)
            word_pieces = self.tokenzier.convert_tokens_to_ids(word_pieces)
            self.word_to_wordpieces.append(word_pieces)
            self.word_pieces_length.append(len(word_pieces))
        self._cls_index = len(vocab)
        self._sep_index = len(vocab) + 1
        self._pad_index = vocab.padding_idx
        self._wordpiece_pad_index = self.tokenzier.convert_tokens_to_ids(['[PAD]'])[0]  # 需要用于生成word_piece
        self.word_to_wordpieces.append(self.tokenzier.convert_tokens_to_ids(['[CLS]']))
        self.word_to_wordpieces.append(self.tokenzier.convert_tokens_to_ids(['[SEP]']))
        self.word_to_wordpieces = np.array(self.word_to_wordpieces, dtype=int)
        print("Successfully generate word pieces.")

    def index_dataset(self, *datasets):
        """
        使用bert的tokenizer将word_pieces与word_pieces_seq_len这两列加入到datasets中，并将他们设置为input。加入的word_piece
            已经包含了[CLS]与[SEP], 且将word_pieces这一列的pad value设置为了bert的pad value。

        :param datasets: DataSet对象
        :return:
        """
        def convert_words_to_word_pieces(words):
            word_pieces = list(chain(*self.word_to_wordpieces[words].tolist()))
            word_pieces = [self._cls_index] + word_pieces + [self._sep_index]
            return word_pieces

        for index, dataset in enumerate(datasets):
            try:
                dataset.apply_field(convert_words_to_word_pieces, field_name='words', new_field_name='word_pieces',
                                    is_input=True)
                dataset.set_pad_val('word_pieces', self._wordpiece_pad_index)
            except Exception as e:
                print(f"Exception happens when processing the {index} dataset.")
                raise e

    def forward(self, word_pieces, token_type_ids=None):
        """

        :param word_pieces: torch.LongTensor, batch_size x max_len
        :param token_type_ids: torch.LongTensor, batch_size x max_len
        :return: num_layers x batch_size x max_len x hidden_size或者num_layers x batch_size x (max_len+2) x hidden_size
        """
        batch_size, max_len = word_pieces.size()

        attn_masks = word_pieces.ne(self._pad_index)
        bert_outputs, _ = self.encoder(word_pieces, token_type_ids=token_type_ids, attention_mask=attn_masks,
                                           output_all_encoded_layers=True)
        # output_layers = [self.layers]  # len(self.layers) x batch_size x max_word_piece_length x hidden_size
        outputs = bert_outputs[0].new_zeros((len(self.layers), batch_size, max_len, bert_outputs[0].size(-1)))
        for l_index, l in enumerate(self.layers):
            outputs[l_index] = bert_outputs[l]
        return outputs