bertmodel.py

import torch.nn as nn
import copy, math
import torch
import numpy as np
import torch.nn.functional as F

class Bert(nn.Module):
    
    def __init__(self, encoder, src_embed):
        super(Bert, self).__init__()

        self.encoder = encoder        
        self.src_embed = src_embed  
    
    def forward(self, src, src_mask):
    
        return self.encoder(self.src_embed(src), src_mask)
    
    
class Encoder(nn.Module):
    "Encoder是N个EncoderLayer的堆积而成"
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        #layer是一个SubLayer，我们clone N个
        self.layers = clones(layer, N)
        #再加一个LayerNorm层
        self.norm = LayerNorm(layer.size)
        
    def forward(self, x, mask):
        "把输入(x,mask)被逐层处理"
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x) #N个EncoderLayer处理完成之后还需要一个LayerNorm
    
class LayerNorm(nn.Module):
    "构建一个layernorm模型"
    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):
    """
    LayerNorm + sublayer(Self-Attenion/Dense) + dropout + 残差连接
    为了简单，把LayerNorm放到了前面，这和原始论文稍有不同，原始论文LayerNorm在最后
    """
    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        #将残差连接应用于具有相同大小的任何子层
        return x + self.dropout(sublayer(self.norm(x)))
    
class EncoderLayer(nn.Module):
    "Encoder由self-attn and feed forward构成"
    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):
        "如上图所示"
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
        return self.sublayer[1](x, self.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))))
    
def make_bert(src_vocab, N=6, d_model=512, d_ff=2048, h=8, dropout=0.1):
    "构建模型"
    c = copy.deepcopy
    attn = MultiHeadedAttention(h, d_model)
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
    position = PositionalEncoding(d_model, dropout)
    model = Bert(
        Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
        
        nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
    )
    
    # 随机初始化参数，这非常重要用Glorot/fan_avg.
    for p in model.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform_(p)
    return model

def make_bert_without_emb(d_model=128, N=2, d_ff=512, h=8, dropout=0.1):
    c = copy.deepcopy
    attn = MultiHeadedAttention(h, d_model)
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
    trainable_encoder = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N)

    return trainable_encoder



def clones(module, N):
    "克隆N个完全相同的SubLayer，使用了copy.deepcopy"
    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])

def subsequent_mask(size):
    "Mask out subsequent positions."
    attn_shape = (1, size, size)
    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
    return torch.from_numpy(subsequent_mask) == 0

def attention(query, key, value, mask=None, dropout=None):
    "计算 '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:
        mask = mask.unsqueeze(-2)
        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):
        "传入head个数及model的维度."
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        # 这里假设d_v=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:
            # 相同的mask适应所有的head.
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)
        
        # 1) 首先使用线性变换，然后把d_model分配给h个Head，每个head为d_k=d_model/h         
        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) 使用attention函数计算scaled-Dot-product-attention 
        x, self.attn = attention(query, key, value, mask=mask, 
                                 dropout=self.dropout)
        
        # 3) 实现Multi-head attention，用view函数把8个head的64维向量拼接成一个512的向量。
        #然后再使用一个线性变换(512,521)，shape不变. 
        x = x.transpose(1, 2).contiguous() \
             .view(nbatches, -1, self.h * self.d_k)
        return self.linears[-1](x)
    
class Embeddings(nn.Module):
    def __init__(self, d_model, vocab):
        super(Embeddings, self).__init__()
        self.lut = nn.Embedding(vocab, d_model)
        self.d_model = d_model

    def forward(self, x):
        return self.lut(x) * math.sqrt(self.d_model)
    
class PositionalEncoding(nn.Module):
    "实现PE函数"
    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 + self.pe[:, :x.size(1)].clone().detach()
        return self.dropout(x)