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)