pytorch bert 源码解读

https://daiwk.github.io/posts/nlp-bert.html

目录

• 概述
• BERT
  • 模型架构
  • Input Representation
  • Pre-training Tasks
    • Task #1: Masked LM
    • Task #2: Next Sentence Prediction
  • Pre-training Procedure
  • Fine-tuning Procedure
  • Comparison of BERT and OpenAI GPT
• 实验
  • GLUE Datasets
    • GLUE Results
  • SQuAD v1.1
  • Named Entity Recognition
  • SWAG
• Ablation Studies
  • Effect of Pre-training Tasks
  • Effect of Model Size
  • Effect of Number of Training Steps
  • Feature-based Approach with BERT
• 代码实现
  • pytorch版本
    • 代码解读
      • 基础知识
      • position encoding
      • position-wise feed forward
      • attention和Multi-head attention
      • layernorm和sublayer
      • 完整的bert
      • vocab和dataset
      • main函数
• 官方版

参考最强NLP预训练模型！谷歌BERT横扫11项NLP任务记录

参考https://www.zhihu.com/question/298203515/answer/509703208

概述

本文介绍了一种新的语言表征模型BERT——来自Transformer的双向编码器表征。与最近的语言表征模型不同，BERT旨在基于所有层的左、右语境来预训练深度双向表征。BERT是首个在大批句子层面和token层面任务中取得当前最优性能的基于微调的表征模型，其性能超越许多使用任务特定架构的系统，刷新了11项NLP任务的当前最优性能记录。

目前将预训练语言表征应用于下游任务存在两种策略：feature-based的策略和fine-tuning策略。

• feature-based策略（如 ELMo）使用将预训练表征作为额外特征的任务专用架构。
• fine-tuning策略（如生成预训练 Transformer (OpenAI GPT)）引入了任务特定最小参数，通过简单地微调预训练参数在下游任务中进行训练。

在之前的研究中，两种策略在预训练期间使用相同的目标函数，利用单向语言模型来学习通用语言表征。

作者认为现有的技术严重制约了预训练表征的能力，微调策略尤其如此。其主要局限在于标准语言模型是单向的，这限制了可以在预训练期间使用的架构类型。例如，OpenAI GPT使用的是从左到右的架构，其中每个token只能注意Transformer自注意力层中的先前token。这些局限对于句子层面的任务而言不是最佳选择，对于token级任务（如 SQuAD 问答）则可能是毁灭性的，因为在这种任务中，结合两个方向的语境至关重要。

BERT（Bidirectional Encoder Representations from Transformers）改进了基于微调的策略。

BERT提出一种新的预训练目标——遮蔽语言模型（masked language model，MLM），来克服上文提到的单向局限。MLM 的灵感来自 Cloze 任务（Taylor, 1953）。MLM随机遮蔽输入中的一些token，目标在于仅基于遮蔽词的语境来预测其原始词汇id。与从左到右的语言模型预训练不同，MLM目标允许表征融合左右两侧的语境，从而预训练一个深度双向Transformer。除了 MLM，我们还引入了一个“下一句预测”（next sentence prediction）任务，该任务联合预训练文本对表征。

贡献：

• 展示了双向预训练语言表征的重要性。不同于 Radford 等人（2018）使用单向语言模型进行预训练，BERT使用MLM预训练深度双向表征。本研究与 Peters 等人（2018）的研究也不同，后者使用的是独立训练的从左到右和从右到左LM的浅层级联。
• 证明了预训练表征可以消除对许多精心设计的任务特定架构的需求。BERT是首个在大批句子层面和token层面任务中取得当前最优性能的基于微调的表征模型，其性能超越许多使用任务特定架构的系统。
• BERT 刷新了11项NLP任务的当前最优性能记录。本论文还报告了BERT的模型简化测试（ablation study），证明该模型的双向特性是最重要的一项新贡献。代码和预训练模型将发布在goo.gl/language/bert。

BERT

模型架构

BERT 旨在基于所有层的左、右语境来预训练深度双向表征。因此，预训练的 BERT 表征可以仅用一个额外的输出层进行微调，进而为很多任务（如问答和语言推断任务）创建当前最优模型，无需对任务特定架构做出大量修改。

BERT 的模型架构是一个多层双向Transformer编码器，基于Vaswani 等人 (2017)描述的原始实现，在tensor2tensor库中发布(当然，可以抽空看看https://daiwk.github.io/posts/platform-tensor-to-tensor.html和https://daiwk.github.io/posts/platform-tensor-to-tensor-coding.html)。

本文中，我们将层数（即Transformer块）表示为\(L\)，将隐层的size表示为\(H\)、自注意力头数表示为\(A\)。在所有实验中，我们将feed-forward/filter的size设置为\(4H\)，即H=768时为3072，H=1024时为4096。我们主要看下在两种模型尺寸上的结果：

• \(BERT_{BASE}\): L=12, H=768, A=12, Total Parameters=110M
• \(BERT_{LARGE}\): L=24, H=1024, A=16, Total Parameters=340M

其中，\(BERT_{BASE}\)和OpenAI GPT的大小是一样的。BERT Transformer使用双向自注意力机制，而GPT Transformer使用受限的自注意力机制，导致每个token只能关注其左侧的语境。双向Transformer在文献中通常称为“Transformer 编码器”，而只关注左侧语境的版本则因能用于文本生成而被称为“Transformer 解码器”。

下图显示了BERT/GPT Transformer/ELMo的结构区别：

• BERT 使用双向Transformer
• OpenAI GPT 使用从左到右的Transformer
• ELMo 使用独立训练的从左到右和从右到左LSTM的级联来生成下游任务的特征。

三种模型中，只有BERT表征会基于所有层中的左右两侧语境。

Input Representation

论文的输入表示（input representation）能够在一个token序列中明确地表示单个文本句子或一对文本句子（例如， [Question, Answer]）。对于给定token，其输入表示通过对相应的token、segment和position embeddings进行求和来构造：

• 使用WordPiece嵌入【GNMT，Google’s neural machine translation system: Bridging the gap between human and machine translation】和30,000个token的词汇表。用##表示分词。
• 使用learned positional embeddings，支持的序列长度最多为512个token。
• 每个序列的第一个token始终是特殊分类嵌入（[CLS]）。对应于该token的最终隐藏状态（即，Transformer的输出）被用作分类任务的聚合序列表示。对于非分类任务，将忽略此向量。
• 句子对被打包成一个序列。以两种方式区分句子。
  • 首先，用特殊标记（[SEP]）将它们分开。
  • 其次，添加一个learned sentence A嵌入到第一个句子的每个token中，一个sentence B嵌入到第二个句子的每个token中。
• 对于单个句子输入，只使用 sentence A嵌入。

Pre-training Tasks

• 它在训练双向语言模型时以减小的概率把少量的词替成了Mask或者另一个随机的词。感觉其目的在于使模型被迫增加对上下文的记忆。（知乎的回答）
• 增加了一个预测下一句的loss。

Task #1: Masked LM

标准条件语言模型只能从左到右或从右到左进行训练，因为双向条件作用将允许每个单词在多层上下文中间接地“see itself”。

为了训练一个深度双向表示（deep bidirectional representation），研究团队采用了一种简单的方法，即随机屏蔽（masking）部分输入token，然后只预测那些被屏蔽的token。论文将这个过程称为“masked LM”(MLM)，尽管在文献中它经常被称为Cloze任务(Taylor, 1953)。

在这个例子中，与masked token对应的最终隐藏向量被输入到词汇表上的输出softmax中，就像在标准LM中一样。在团队所有实验中，随机地屏蔽了每个序列中15%的WordPiece token。与去噪的自动编码器（Vincent et al.， 2008）相反，只预测masked words而不是重建整个输入。

虽然这确实能让团队获得双向预训练模型，但这种方法有两个缺点。

• 缺点1：预训练和finetuning之间不匹配，因为在finetuning期间从未看到[MASK]token。

为了解决这个问题，团队并不总是用实际的[MASK]token替换被“masked”的词汇。相反，训练数据生成器随机选择15％的token。

例如在这个句子“my dog is hairy”中，它选择的token是“hairy”。然后，执行以下过程：

数据生成器将执行以下操作，而不是始终用[MASK]替换所选单词：

• 80％的时间：用[MASK]标记替换单词，例如，my dog is hairy → my dog is [MASK]
• 10％的时间：用一个随机的单词替换该单词，例如，my dog is hairy → my dog is apple
• 10％的时间：保持单词不变，例如，my dog is hairy → my dog is hairy. 这样做的目的是将表示偏向于实际观察到的单词。

Transformer encoder不知道它将被要求预测哪些单词或哪些单词已被随机单词替换，因此它被迫保持每个输入token的分布式上下文表示。此外，因为随机替换只发生在所有token的1.5％（即15％的10％），这似乎不会损害模型的语言理解能力。

• 缺点2：每个batch只预测了15％的token，这表明模型可能需要更多的预训练步骤才能收敛。

团队证明MLM的收敛速度略慢于 left-to-right的模型（预测每个token），但MLM模型在实验上获得的提升远远超过增加的训练成本。

Task #2: Next Sentence Prediction

在为了训练一个理解句子的模型关系，预先训练一个二分类的下一句测任务，这一任务可以从任何单语语料库中生成。具体地说，当选择句子A和B作为预训练样本时，B有50％的可能是A的下一个句子，也有50％的可能是来自语料库的随机句子。例如：

Input =
[CLS] the man went to [MASK] store [SEP]
he bought a gallon [MASK] milk [SEP]
Label = IsNext

Input =
[CLS] the man [MASK] to the store [SEP]
penguin [MASK] are flight ##less birds [SEP]
Label = NotNext

完全随机地选择了NotNext语句，最终的预训练模型在此任务上实现了97％-98％的准确率。

Pre-training Procedure

使用gelu激活函数（Bridging nonlinearities and stochastic regularizers with gaus- sian error linear units），在pytorch里实现如下：

class GELU(nn.Module):
    """
    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
    """

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

Fine-tuning Procedure

Comparison of BERT and OpenAI GPT

实验

网络结构如下：

GLUE Datasets

GLUE Results

SQuAD v1.1

Named Entity Recognition

SWAG

Ablation Studies

Effect of Pre-training Tasks

Effect of Model Size

Effect of Number of Training Steps

Feature-based Approach with BERT

代码实现

pytorch版本

https://github.com/codertimo/BERT-pytorch

fork了一份：https://github.com/daiwk/BERT-pytorch

输入data/corpus.small：

Welcome to the \t the jungle \n
I can stay \t here all night \n

可视化，需要：

brew install graphviz # mac
pip3 install git+https://github.com/szagoruyko/pytorchviz

画出bert的架构图的方法(先生成vocab，如果机器的dot不支持pdf，只支持png/jpg等，需要在lib/python3.6/site-packages/torchviz/dot.py中把dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))改成dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"), format="png"))：

import torch
from torch import nn
from torchviz import make_dot, make_dot_from_trace

import sys

sys.path.append("./bert_pytorch-0.0.1a4.src/")
#from trainer import BERTTrainer
from model import BERTLM, BERT
from dataset import BERTDataset, WordVocab
from torch.utils.data import DataLoader

def demo():
    lstm_cell = nn.LSTMCell(128, 128)
    x = torch.randn(1, 128)
    dot = make_dot(lstm_cell(x), params=dict(list(lstm_cell.named_parameters())))
    file_out = "xx"
    dot.render(file_out)

def bert_dot():
    """
    """
    vocab_size = 128
    train_dataset_path = "data/bert_train_data.xxx"
    vocab_path = "data/vocab.all.xxx"
    vocab = WordVocab.load_vocab(vocab_path)

    train_dataset = BERTDataset(train_dataset_path, vocab, seq_len=20,
                                corpus_lines=2000, on_memory=True)

    train_data_loader = DataLoader(train_dataset, batch_size=8, num_workers=8)
    bert = BERT(len(vocab), hidden=256, n_layers=8, attn_heads=8)
    device = torch.device("cpu")
    mymodel = BERTLM(bert, vocab_size).to(device)
    data_iter = train_data_loader
    out_idx = 0
    for data in data_iter:
        data = {key: value.to(device) for key, value in data.items()}
        if out_idx == 0:
            g = make_dot(mymodel(data["bert_input"], data["segment_label"]), params=dict(mymodel.named_parameters()))
            g.render("./bert-arch")
            break

bert_dot()

可以画出这么个图。。图太大，自己下载看看

https://daiwk.github.io/assets/bert-arch.jpeg

对应的pdf如

https://daiwk.github.io/assets/bert-arch.pdf

对应的dot文件

https://daiwk.github.io/assets/bert-arch

把dot文件转换成其他格式的方式：

input=./bert-arch
output=./bert-arch
dot $input -Tjpeg -o $output.jpeg
dot $input -Tpdf -o $output.pdf

设置一个layer的简单版pdf如下：

https://daiwk.github.io/assets/bert-arch-1layer.pdf

代码解读

transformer部分参考http://nlp.seas.harvard.edu/2018/04/03/attention.htm

可以学习下https://blog.csdn.net/stupid_3/article/details/83184691，讲得很细致呢！

基础知识

参考https://daiwk.github.io/posts/knowledge-pytorch-usage.html

position encoding

代码

class PositionalEncoding(nn.Module):
    "Implement the PE function."
    def __init__(self, d_model, dropout, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) *
                             -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + Variable(self.pe[:, :x.size(1)],
                         requires_grad=False)
        return self.dropout(x)

输入是shape为(max_len, d_model)的矩阵，d_model是emb的size。如下图，输入是一个max_len=100，d_model=20的矩阵，图中画的是这20维里的4、5、6、7每一维在100个position的取值。

bert里改名了一下：

class PositionalEmbedding(nn.Module):

    def __init__(self, d_model, max_len=512):
        super().__init__()

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model).float()
        pe.require_grad = False

        position = torch.arange(0, max_len).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp()

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        return self.pe[:, :x.size(1)]

而bert还有另外两个embedding，就是segment和token，这里用很简单的实现：

class SegmentEmbedding(nn.Embedding):
    def __init__(self, embed_size=512):
        ### 输入是segment_label，表示是第1句话，第2句话，还是padding，所以num_embeddings是3
        super().__init__(3, embed_size, padding_idx=0)

class TokenEmbedding(nn.Embedding):
    def __init__(self, vocab_size, embed_size=512):
        super().__init__(vocab_size, embed_size, padding_idx=0)

用的时候是把三者加起来：

class BERTEmbedding(nn.Module):
    """
    BERT Embedding which is consisted with under features
        1. TokenEmbedding : normal embedding matrix
        2. PositionalEmbedding : adding positional information using sin, cos
        2. SegmentEmbedding : adding sentence segment info, (sent_A:1, sent_B:2)

        sum of all these features are output of BERTEmbedding
    """

    def __init__(self, vocab_size, embed_size, dropout=0.1):
        """
        :param vocab_size: total vocab size
        :param embed_size: embedding size of token embedding
        :param dropout: dropout rate
        """
        super().__init__()
        self.token = TokenEmbedding(vocab_size=vocab_size, embed_size=embed_size)
        self.position = PositionalEmbedding(d_model=self.token.embedding_dim)
        self.segment = SegmentEmbedding(embed_size=self.token.embedding_dim)
        self.dropout = nn.Dropout(p=dropout)
        self.embed_size = embed_size

    def forward(self, sequence, segment_label):
        x = self.token(sequence) + self.position(sequence) + self.segment(segment_label)
        return self.dropout(x)

这部分画出来的图就应该是下面这个了：

position-wise feed forward

class PositionwiseFeedForward(nn.Module):
    "Implements FFN equation."
    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.w_2(self.dropout(F.relu(self.w_1(x))))

在bert中，把relu改为gelu，所以：

class GELU(nn.Module):
    """
    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
    """

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

class PositionwiseFeedForward(nn.Module):
    "Implements FFN equation."

    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = GELU()

    def forward(self, x):
        return self.w_2(self.dropout(self.activation(self.w_1(x))))

attention和Multi-head attention

代码如下：

def attention(query, key, value, mask=None, dropout=None):
    "Compute 'Scaled Dot Product Attention'"
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) \
             / math.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask == 0, -1e9)
    p_attn = F.softmax(scores, dim = -1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    return torch.matmul(p_attn, value), p_attn

class MultiHeadedAttention(nn.Module):
    def __init__(self, h, d_model, dropout=0.1):
        "Take in model size and number of heads."
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        # We assume d_v always equals d_k
        self.d_k = d_model // h
        self.h = h

        self.linears = clones(nn.Linear(d_model, d_model), 4)
        self.attn = None
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        "Implements Figure 2"
        if mask is not None:
            # Same mask applied to all h heads.
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)

        # 1) Do all the linear projections in batch from d_model => h x d_k
        query, key, value = \
            [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
             for l, x in zip(self.linears, (query, key, value))]

        # 2) Apply attention on all the projected vectors in batch.
        x, self.attn = attention(query, key, value, mask=mask,
                                 dropout=self.dropout)

        # 3) "Concat" using a view and apply a final linear.
        x = x.transpose(1, 2).contiguous() \
             .view(nbatches, -1, self.h * self.d_k)
        return self.linears[-1](x)

注：

画出来的图可以参考[https://daiwk.github.io/assets/bert-arch-1layer.pdf]

有4个Linear，其中三个分别和q,k,v相乘，最后一个和concat后的相乘。大小都是d_model,d_model。因为d_k=d_v=d_model/h，对于q来讲，有h个(d_k, d_model)，所以一个(d_model, d_model)就行了。k,v同理。当然，后面还搞了下batches，所以画出来的图是q和k先bmm一下，再和v去bmm一下，最后的concat是就是view一下，然后再和最后那个linear去mm一下。

封装一下：

class Attention(nn.Module):
    """
    Compute 'Scaled Dot Product Attention
    """

    def forward(self, query, key, value, mask=None, dropout=None):
        scores = torch.matmul(query, key.transpose(-2, -1)) \
                 / math.sqrt(query.size(-1))

        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)

        p_attn = F.softmax(scores, dim=-1)

        if dropout is not None:
            p_attn = dropout(p_attn)

        return torch.matmul(p_attn, value), p_attn

class MultiHeadedAttention(nn.Module):
    """
    Take in model size and number of heads.
    """

    def __init__(self, h, d_model, dropout=0.1):
        super().__init__()
        assert d_model % h == 0

        # We assume d_v always equals d_k
        self.d_k = d_model // h
        self.h = h

        self.linear_layers = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])
        self.output_linear = nn.Linear(d_model, d_model)
        self.attention = Attention()

        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)

        # 1) Do all the linear projections in batch from d_model => h x d_k
        query, key, value = [l(x).view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
                             for l, x in zip(self.linear_layers, (query, key, value))]

        # 2) Apply attention on all the projected vectors in batch.
        x, attn = self.attention(query, key, value, mask=mask, dropout=self.dropout)

        # 3) "Concat" using a view and apply a final linear.
        x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.h * self.d_k)

        return self.output_linear(x)

layernorm和sublayer

class LayerNorm(nn.Module):
    "Construct a layernorm module (See citation for details)."
    def __init__(self, features, eps=1e-6):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2

class SublayerConnection(nn.Module):
    """
    A residual connection followed by a layer norm.
    Note for code simplicity the norm is first as opposed to last.
    """
    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        "Apply residual connection to any sublayer with the same size."
        return x + self.dropout(sublayer(self.norm(x)))

transformer里的encoder：

class EncoderLayer(nn.Module):
    "Encoder is made up of self-attn and feed forward (defined below)"
    def __init__(self, size, self_attn, feed_forward, dropout):
        super(EncoderLayer, self).__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x, mask):
        "Follow Figure 1 (left) for connections."
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
        return self.sublayer[1](x, self.feed_forward)

decoder部分：

class Decoder(nn.Module):
    "Generic N layer decoder with masking."
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)

class DecoderLayer(nn.Module):
    "Decoder is made of self-attn, src-attn, and feed forward (defined below)"
    def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
        super(DecoderLayer, self).__init__()
        self.size = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 3)

    def forward(self, x, memory, src_mask, tgt_mask):
        "Follow Figure 1 (right) for connections."
        m = memory
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
        x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
        return self.sublayer[2](x, self.feed_forward)

其中的mask部分：

def subsequent_mask(size):
    "Mask out subsequent positions."
    attn_shape = (1, size, size)
    ## np.triu：一个上三角矩阵（注意：这里是一个方阵）右上角都是1，左下角都是0
    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
    return torch.from_numpy(subsequent_mask) == 0

class Batch:
    "Object for holding a batch of data with mask during training."
    def __init__(self, src, trg=None, pad=0):
        self.src = src
        self.src_mask = (src != pad).unsqueeze(-2)
        if trg is not None:
            self.trg = trg[:, :-1]
            self.trg_y = trg[:, 1:]
            self.trg_mask = \
                self.make_std_mask(self.trg, pad)
            self.ntokens = (self.trg_y != pad).data.sum()

    @staticmethod
    def make_std_mask(tgt, pad):
        "Create a mask to hide padding and future words."
        tgt_mask = (tgt != pad).unsqueeze(-2)
        tgt_mask = tgt_mask & Variable(
            subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))
        return tgt_mask

在产出数据时把mask加上：

def data_gen(V, batch, nbatches):
    "Generate random data for a src-tgt copy task."
    for i in range(nbatches):
        data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))
        data[:, 0] = 1
        src = Variable(data, requires_grad=False)
        tgt = Variable(data, requires_grad=False)
        yield Batch(src, tgt, 0)

整个模型：

class EncoderDecoder(nn.Module):
    """
    A standard Encoder-Decoder architecture. Base for this and many
    other models.
    """
    def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
        super(EncoderDecoder, self).__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.src_embed = src_embed
        self.tgt_embed = tgt_embed
        self.generator = generator

    def forward(self, src, tgt, src_mask, tgt_mask):
        "Take in and process masked src and target sequences."
        return self.decode(self.encode(src, src_mask), src_mask,
                            tgt, tgt_mask)

    def encode(self, src, src_mask):
        return self.encoder(self.src_embed(src), src_mask)

    def decode(self, memory, src_mask, tgt, tgt_mask):
        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)

class Generator(nn.Module):
    "Define standard linear + softmax generation step."
    def __init__(self, d_model, vocab):
        super(Generator, self).__init__()
        self.proj = nn.Linear(d_model, vocab)

    def forward(self, x):
        return F.log_softmax(self.proj(x), dim=-1)

def make_model(src_vocab, tgt_vocab, N=6,
               d_model=512, d_ff=2048, h=8, dropout=0.1):
    "Helper: Construct a model from hyperparameters."
    c = copy.deepcopy
    attn = MultiHeadedAttention(h, d_model)
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
    position = PositionalEncoding(d_model, dropout)
    model = EncoderDecoder(
        Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
        Decoder(DecoderLayer(d_model, c(attn), c(attn),
                             c(ff), dropout), N),
        nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
        nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
        Generator(d_model, tgt_vocab))

    # This was important from their code.
    # Initialize parameters with Glorot / fan_avg.
    for p in model.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform(p)
    return model

bert中的transformerblock(相当于只有encoder，但是加入了自己的mask)：

class TransformerBlock(nn.Module):
    """
    Bidirectional Encoder = Transformer (self-attention)
    Transformer = MultiHead_Attention + Feed_Forward with sublayer connection
    """

    def __init__(self, hidden, attn_heads, feed_forward_hidden, dropout):
        """
        :param hidden: hidden size of transformer
        :param attn_heads: head sizes of multi-head attention
        :param feed_forward_hidden: feed_forward_hidden, usually 4*hidden_size
        :param dropout: dropout rate
        """

        super().__init__()
        self.attention = MultiHeadedAttention(h=attn_heads, d_model=hidden)
        self.feed_forward = PositionwiseFeedForward(d_model=hidden, d_ff=feed_forward_hidden, dropout=dropout)
        self.input_sublayer = SublayerConnection(size=hidden, dropout=dropout)
        self.output_sublayer = SublayerConnection(size=hidden, dropout=dropout)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, mask):
        x = self.input_sublayer(x, lambda _x: self.attention.forward(_x, _x, _x, mask=mask))
        x = self.output_sublayer(x, self.feed_forward)
        return self.dropout(x)

完整的bert

class BERT(nn.Module):
    """
    BERT model : Bidirectional Encoder Representations from Transformers.
    """

    def __init__(self, vocab_size, hidden=768, n_layers=12, attn_heads=12, dropout=0.1):
        """
        :param vocab_size: vocab_size of total words
        :param hidden: BERT model hidden size
        :param n_layers: numbers of Transformer blocks(layers)
        :param attn_heads: number of attention heads
        :param dropout: dropout rate
        """

        super().__init__()
        self.hidden = hidden
        self.n_layers = n_layers
        self.attn_heads = attn_heads

        # paper noted they used 4*hidden_size for ff_network_hidden_size
        self.feed_forward_hidden = hidden * 4

        # embedding for BERT, sum of positional, segment, token embeddings
        self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=hidden)

        # multi-layers transformer blocks, deep network
        self.transformer_blocks = nn.ModuleList(
            [TransformerBlock(hidden, attn_heads, hidden * 4, dropout) for _ in range(n_layers)])

    def forward(self, x, segment_info):
        # attention masking for padded token
        # torch.ByteTensor([batch_size, 1, seq_len, seq_len)
        mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1)

        # embedding the indexed sequence to sequence of vectors
        x = self.embedding(x, segment_info)

        # running over multiple transformer blocks
        for transformer in self.transformer_blocks:
            x = transformer.forward(x, mask)

        return x

对于pretrain来讲：

class BERTLM(nn.Module):
    """
    BERT Language Model
    Next Sentence Prediction Model + Masked Language Model
    """

    def __init__(self, bert: BERT, vocab_size):
        """
        :param bert: BERT model which should be trained
        :param vocab_size: total vocab size for masked_lm
        """

        super().__init__()
        self.bert = bert
        self.next_sentence = NextSentencePrediction(self.bert.hidden)
        self.mask_lm = MaskedLanguageModel(self.bert.hidden, vocab_size)

    def forward(self, x, segment_label):
        x = self.bert(x, segment_label)
        return self.next_sentence(x), self.mask_lm(x)

class NextSentencePrediction(nn.Module):
    """
    2-class classification model : is_next, is_not_next
    """

    def __init__(self, hidden):
        """
        :param hidden: BERT model output size
        """
        super().__init__()
        self.linear = nn.Linear(hidden, 2)
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        return self.softmax(self.linear(x[:, 0]))

class MaskedLanguageModel(nn.Module):
    """
    predicting origin token from masked input sequence
    n-class classification problem, n-class = vocab_size
    """

    def __init__(self, hidden, vocab_size):
        """
        :param hidden: output size of BERT model
        :param vocab_size: total vocab size
        """
        super().__init__()
        self.linear = nn.Linear(hidden, vocab_size)
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        return self.softmax(self.linear(x))

整个训练过程：

class BERTTrainer:
    """
    BERTTrainer make the pretrained BERT model with two LM training method.

        1. Masked Language Model : 3.3.1 Task #1: Masked LM
        2. Next Sentence prediction : 3.3.2 Task #2: Next Sentence Prediction

    please check the details on README.md with simple example.

    """

    def __init__(self, bert: BERT, vocab_size: int,
                 train_dataloader: DataLoader, test_dataloader: DataLoader = None,
                 lr: float = 1e-4, betas=(0.9, 0.999), weight_decay: float = 0.01, warmup_steps=10000,
                 with_cuda: bool = True, cuda_devices=None, log_freq: int = 10):
        """
        :param bert: BERT model which you want to train
        :param vocab_size: total word vocab size
        :param train_dataloader: train dataset data loader
        :param test_dataloader: test dataset data loader [can be None]
        :param lr: learning rate of optimizer
        :param betas: Adam optimizer betas
        :param weight_decay: Adam optimizer weight decay param
        :param with_cuda: traning with cuda
        :param log_freq: logging frequency of the batch iteration
        """

        # Setup cuda device for BERT training, argument -c, --cuda should be true
        cuda_condition = torch.cuda.is_available() and with_cuda
        self.device = torch.device("cuda:0" if cuda_condition else "cpu")

        # This BERT model will be saved every epoch
        self.bert = bert
        # Initialize the BERT Language Model, with BERT model
        self.model = BERTLM(bert, vocab_size).to(self.device)

        # Distributed GPU training if CUDA can detect more than 1 GPU
        if with_cuda and torch.cuda.device_count() > 1:
            print("Using %d GPUS for BERT" % torch.cuda.device_count())
            self.model = nn.DataParallel(self.model, device_ids=cuda_devices)

        # Setting the train and test data loader
        self.train_data = train_dataloader
        self.test_data = test_dataloader

        # Setting the Adam optimizer with hyper-param
        self.optim = Adam(self.model.parameters(), lr=lr, betas=betas, weight_decay=weight_decay)
        self.optim_schedule = ScheduledOptim(self.optim, self.bert.hidden, n_warmup_steps=warmup_steps)

        # Using Negative Log Likelihood Loss function for predicting the masked_token
        self.criterion = nn.NLLLoss(ignore_index=0)

        self.log_freq = log_freq

        print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()]))

    def train(self, epoch):
        self.iteration(epoch, self.train_data)

    def test(self, epoch):
        self.iteration(epoch, self.test_data, train=False)

    def iteration(self, epoch, data_loader, train=True):
        """
        loop over the data_loader for training or testing
        if on train status, backward operation is activated
        and also auto save the model every peoch

        :param epoch: current epoch index
        :param data_loader: torch.utils.data.DataLoader for iteration
        :param train: boolean value of is train or test
        :return: None
        """
        str_code = "train" if train else "test"

        # Setting the tqdm progress bar
        data_iter = tqdm.tqdm(enumerate(data_loader),
                              desc="EP_%s:%d" % (str_code, epoch),
                              total=len(data_loader),
                              bar_format="{l_bar}{r_bar}")

        avg_loss = 0.0
        total_correct = 0
        total_element = 0

        for i, data in data_iter:
            # 0. batch_data will be sent into the device(GPU or cpu)
            data = {key: value.to(self.device) for key, value in data.items()}

            # 1. forward the next_sentence_prediction and masked_lm model
            next_sent_output, mask_lm_output = self.model.forward(data["bert_input"], data["segment_label"])

            # 2-1. NLL(negative log likelihood) loss of is_next classification result
            next_loss = self.criterion(next_sent_output, data["is_next"])

            # 2-2. NLLLoss of predicting masked token word
            mask_loss = self.criterion(mask_lm_output.transpose(1, 2), data["bert_label"])

            # 2-3. Adding next_loss and mask_loss : 3.4 Pre-training Procedure
            loss = next_loss + mask_loss

            # 3. backward and optimization only in train
            if train:
                self.optim_schedule.zero_grad()
                loss.backward()
                self.optim_schedule.step_and_update_lr()

            # next sentence prediction accuracy
            correct = next_sent_output.argmax(dim=-1).eq(data["is_next"]).sum().item()
            avg_loss += loss.item()
            total_correct += correct
            total_element += data["is_next"].nelement()

            post_fix = {
                "epoch": epoch,
                "iter": i,
                "avg_loss": avg_loss / (i + 1),
                "avg_acc": total_correct / total_element * 100,
                "loss": loss.item()
            }

            if i % self.log_freq == 0:
                data_iter.write(str(post_fix))

        print("EP%d_%s, avg_loss=" % (epoch, str_code), avg_loss / len(data_iter), "total_acc=",
              total_correct * 100.0 / total_element)

    def save(self, epoch, file_path="output/bert_trained.model"):
        """
        Saving the current BERT model on file_path

        :param epoch: current epoch number
        :param file_path: model output path which gonna be file_path+"ep%d" % epoch
        :return: final_output_path
        """
        output_path = file_path + ".ep%d" % epoch
        torch.save(self.bert.cpu(), output_path)
        self.bert.to(self.device)
        print("EP:%d Model Saved on:" % epoch, output_path)
        return output_path

vocab和dataset

vocab部分：

from collections import Counter

class TorchVocab(object):
    """Defines a vocabulary object that will be used to numericalize a field.
    Attributes:
        freqs: A collections.Counter object holding the frequencies of tokens
            in the data used to build the Vocab.
        stoi: A collections.defaultdict instance mapping token strings to
            numerical identifiers.
        itos: A list of token strings indexed by their numerical identifiers.
    """

    def __init__(self, counter, max_size=None, min_freq=1, specials=['<pad>', '<oov>'],
                 vectors=None, unk_init=None, vectors_cache=None):
        """Create a Vocab object from a collections.Counter.
        Arguments:
            counter: collections.Counter object holding the frequencies of
                each value found in the data.
            max_size: The maximum size of the vocabulary, or None for no
                maximum. Default: None.
            min_freq: The minimum frequency needed to include a token in the
                vocabulary. Values less than 1 will be set to 1. Default: 1.
            specials: The list of special tokens (e.g., padding or eos) that
                will be prepended to the vocabulary in addition to an <unk>
                token. Default: ['<pad>']
            vectors: One of either the available pretrained vectors
                or custom pretrained vectors (see Vocab.load_vectors);
                or a list of aforementioned vectors
            unk_init (callback): by default, initialize out-of-vocabulary word vectors
                to zero vectors; can be any function that takes in a Tensor and
                returns a Tensor of the same size. Default: torch.Tensor.zero_
            vectors_cache: directory for cached vectors. Default: '.vector_cache'
        """
        self.freqs = counter
        counter = counter.copy()
        min_freq = max(min_freq, 1)

        self.itos = list(specials)
        # frequencies of special tokens are not counted when building vocabulary
        # in frequency order
        for tok in specials:
            del counter[tok]

        max_size = None if max_size is None else max_size + len(self.itos)

        # sort by frequency, then alphabetically
        words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0])
        words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True)

        for word, freq in words_and_frequencies:
            if freq < min_freq or len(self.itos) == max_size:
                break
            self.itos.append(word)

        # stoi is simply a reverse dict for itos
        self.stoi = {tok: i for i, tok in enumerate(self.itos)}

        self.vectors = None
        if vectors is not None:
            self.load_vectors(vectors, unk_init=unk_init, cache=vectors_cache)
        else:
            assert unk_init is None and vectors_cache is None

    def __eq__(self, other):
        if self.freqs != other.freqs:
            return False
        if self.stoi != other.stoi:
            return False
        if self.itos != other.itos:
            return False
        if self.vectors != other.vectors:
            return False
        return True

    def __len__(self):
        return len(self.itos)

    def vocab_rerank(self):
        self.stoi = {word: i for i, word in enumerate(self.itos)}

    def extend(self, v, sort=False):
        words = sorted(v.itos) if sort else v.itos
        for w in words:
            if w not in self.stoi:
                self.itos.append(w)
                self.stoi[w] = len(self.itos) - 1

class Vocab(TorchVocab):
    def __init__(self, counter, max_size=None, min_freq=1):
        self.pad_index = 0
        self.unk_index = 1
        self.eos_index = 2
        self.sos_index = 3
        self.mask_index = 4
        super().__init__(counter, specials=["<pad>", "<unk>", "<eos>", "<sos>", "<mask>"],
                         max_size=max_size, min_freq=min_freq)

    def to_seq(self, sentece, seq_len, with_eos=False, with_sos=False) -> list:
        pass

    def from_seq(self, seq, join=False, with_pad=False):
        pass

    @staticmethod
    def load_vocab(vocab_path: str) -> 'Vocab':
        with open(vocab_path, "rb") as f:
            return pickle.load(f)

    def save_vocab(self, vocab_path):
        with open(vocab_path, "wb") as f:
            pickle.dump(self, f)

# Building Vocab with text files
class WordVocab(Vocab):
    def __init__(self, texts, max_size=None, min_freq=1):
        print("Building Vocab")
        counter = Counter()
        for line in tqdm.tqdm(texts):
            if isinstance(line, list):
                words = line
            else:
                words = line.replace("\n", "").replace("\t", "").split()

            for word in words:
                counter[word] += 1
        super().__init__(counter, max_size=max_size, min_freq=min_freq)

    def to_seq(self, sentence, seq_len=None, with_eos=False, with_sos=False, with_len=False):
        if isinstance(sentence, str):
            sentence = sentence.split()

        seq = [self.stoi.get(word, self.unk_index) for word in sentence]

        if with_eos:
            seq += [self.eos_index]  # this would be index 1
        if with_sos:
            seq = [self.sos_index] + seq

        origin_seq_len = len(seq)

        if seq_len is None:
            pass
        elif len(seq) <= seq_len:
            seq += [self.pad_index for _ in range(seq_len - len(seq))]
        else:
            seq = seq[:seq_len]

        return (seq, origin_seq_len) if with_len else seq

    def from_seq(self, seq, join=False, with_pad=False):
        words = [self.itos[idx]
                 if idx < len(self.itos)
                 else "<%d>" % idx
                 for idx in seq
                 if not with_pad or idx != self.pad_index]

        return " ".join(words) if join else words

    @staticmethod
    def load_vocab(vocab_path: str) -> 'WordVocab':
        with open(vocab_path, "rb") as f:
            return pickle.load(f)

def build():
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("-c", "--corpus_path", required=True, type=str)
    parser.add_argument("-o", "--output_path", required=True, type=str)
    parser.add_argument("-s", "--vocab_size", type=int, default=None)
    parser.add_argument("-e", "--encoding", type=str, default="utf-8")
    parser.add_argument("-m", "--min_freq", type=int, default=1)
    args = parser.parse_args()

    with open(args.corpus_path, "r", encoding=args.encoding) as f:
        vocab = WordVocab(f, max_size=args.vocab_size, min_freq=args.min_freq)

    print("VOCAB SIZE:", len(vocab))
    vocab.save_vocab(args.output_path)

main函数

    print("Loading Vocab", args.vocab_path)
    vocab = WordVocab.load_vocab(args.vocab_path)
    print("Vocab Size: ", len(vocab))

    print("Loading Train Dataset", args.train_dataset)
    train_dataset = BERTDataset(args.train_dataset, vocab, seq_len=args.seq_len,
                                corpus_lines=args.corpus_lines, on_memory=args.on_memory)

    print("Loading Test Dataset", args.test_dataset)
    test_dataset = BERTDataset(args.test_dataset, vocab, seq_len=args.seq_len, on_memory=args.on_memory) \
        if args.test_dataset is not None else None

    print("Creating Dataloader")
    train_data_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers)
    test_data_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers) \
        if test_dataset is not None else None

    print("Building BERT model")
    bert = BERT(len(vocab), hidden=args.hidden, n_layers=args.layers, attn_heads=args.attn_heads)

    print("Creating BERT Trainer")
    trainer = BERTTrainer(bert, len(vocab), train_dataloader=train_data_loader, test_dataloader=test_data_loader,
                          lr=args.lr, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay,
                          with_cuda=args.with_cuda, cuda_devices=args.cuda_devices, log_freq=args.log_freq)

    print("Training Start")
    for epoch in range(args.epochs):
        trainer.train(epoch)
        trainer.save(epoch, args.output_path)

        if test_data_loader is not None:
            trainer.test(epoch)

dataset部分：

from torch.utils.data import Dataset
import tqdm
import torch
import random

class BERTDataset(Dataset):
    def __init__(self, corpus_path, vocab, seq_len, encoding="utf-8", corpus_lines=None, on_memory=True):
        self.vocab = vocab
        self.seq_len = seq_len

        self.on_memory = on_memory
        self.corpus_lines = corpus_lines
        self.corpus_path = corpus_path
        self.encoding = encoding

        with open(corpus_path, "r", encoding=encoding) as f:
            if self.corpus_lines is None and not on_memory:
                for _ in tqdm.tqdm(f, desc="Loading Dataset", total=corpus_lines):
                    self.corpus_lines += 1

            if on_memory:
                self.lines = [line[:-1].split("\t")
                              for line in tqdm.tqdm(f, desc="Loading Dataset", total=corpus_lines)]
                self.corpus_lines = len(self.lines)

        if not on_memory:
            self.file = open(corpus_path, "r", encoding=encoding)
            self.random_file = open(corpus_path, "r", encoding=encoding)

            for _ in range(random.randint(self.corpus_lines if self.corpus_lines < 1000 else 1000)):
                self.random_file.__next__()

    def __len__(self):
        return self.corpus_lines

    def __getitem__(self, item):
        t1, t2, is_next_label = self.random_sent(item)
        t1_random, t1_label = self.random_word(t1)
        t2_random, t2_label = self.random_word(t2)

        # [CLS] tag = SOS tag, [SEP] tag = EOS tag
        t1 = [self.vocab.sos_index] + t1_random + [self.vocab.eos_index]
        t2 = t2_random + [self.vocab.eos_index]

        t1_label = [self.vocab.pad_index] + t1_label + [self.vocab.pad_index]
        t2_label = t2_label + [self.vocab.pad_index]

        segment_label = ([1 for _ in range(len(t1))] + [2 for _ in range(len(t2))])[:self.seq_len]
        bert_input = (t1 + t2)[:self.seq_len]
        bert_label = (t1_label + t2_label)[:self.seq_len]

        padding = [self.vocab.pad_index for _ in range(self.seq_len - len(bert_input))]
        bert_input.extend(padding), bert_label.extend(padding), segment_label.extend(padding)

        output = {"bert_input": bert_input,
                  "bert_label": bert_label,
                  "segment_label": segment_label,
                  "is_next": is_next_label}

        return {key: torch.tensor(value) for key, value in output.items()}

    def random_word(self, sentence):
        tokens = sentence.split()
        output_label = []

        for i, token in enumerate(tokens):
            prob = random.random()
            if prob < 0.15:
                prob /= 0.15

                # 80% randomly change token to mask token
                if prob < 0.8:
                    tokens[i] = self.vocab.mask_index

                # 10% randomly change token to random token
                elif prob < 0.9:
                    tokens[i] = random.randrange(len(self.vocab))

                # 10% randomly change token to current token
                else:
                    tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)

                output_label.append(self.vocab.stoi.get(token, self.vocab.unk_index))

            else:
                tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)
                output_label.append(0)

        return tokens, output_label

    def random_sent(self, index):
        t1, t2 = self.get_corpus_line(index)

        # output_text, label(isNotNext:0, isNext:1)
        if random.random() > 0.5:
            return t1, t2, 1
        else:
            return t1, self.get_random_line(), 0

    def get_corpus_line(self, item):
        if self.on_memory:
            return self.lines[item][0], self.lines[item][1]
        else:
            line = self.file.__next__()
            if line is None:
                self.file.close()
                self.file = open(self.corpus_path, "r", encoding=self.encoding)
                line = self.file.__next__()

            t1, t2 = line[:-1].split("\t")
            return t1, t2

    def get_random_line(self):
        if self.on_memory:
            return self.lines[random.randrange(len(self.lines))][1]

        line = self.file.__next__()
        if line is None:
            self.file.close()
            self.file = open(self.corpus_path, "r", encoding=self.encoding)
            for _ in range(random.randint(self.corpus_lines if self.corpus_lines < 1000 else 1000)):
                self.random_file.__next__()
            line = self.random_file.__next__()
        return line[:-1].split("\t")[1]

官方版

https://github.com/google-research/bert

详见https://daiwk.github.io/posts/nlp-bert-code.html

原创文章，转载请注明出处！ 
本文链接：http://daiwk.github.io/posts/nlp-bert.html

上篇： TensorFlow Serving的深度学习在线预估优化