# -*- coding: utf-8 -*- # AUTHOR: Shun Zheng # DATE: 19-9-19 # Code Reference: http://nlp.seas.harvard.edu/2019/04/03/attention.html import copy import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import math def clones(module, N): """Produce N identical layers.""" return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) 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) 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)) # fit for bert optimizer self.gamma = nn.Parameter(torch.ones(features)) self.beta = 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 return self.gamma * (x - mean) / (std + self.eps) + self.beta class Encoder(nn.Module): """"Core encoder is a stack of N layers""" def __init__(self, layer, N): super(Encoder, self).__init__() self.layers = clones(layer, N) self.norm = LayerNorm(layer.size) def forward(self, x, mask): """Pass the input (and mask) through each layer in turn.""" for layer in self.layers: x = layer(x, mask) return self.norm(x) 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))) 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) 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) def subsequent_mask(size): """Mask out subsequent positions.""" attn_shape = (1, size, size) subseq_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') return torch.from_numpy(subseq_mask) == 0 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) 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)))) 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): """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 + self.pe[:, :x.size(1)].to(device=x.device) return self.dropout(x) def make_model(src_vocab, tgt_vocab, num_layers=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), num_layers), Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), num_layers), 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 def make_transformer_encoder(num_layers, hidden_size, ff_size=2048, num_att_heads=8, dropout=0.1): dcopy = copy.deepcopy mh_att = MultiHeadedAttention(num_att_heads, hidden_size, dropout=dropout) pos_ff = PositionwiseFeedForward(hidden_size, ff_size, dropout=dropout) tranformer_encoder = Encoder( EncoderLayer(hidden_size, dcopy(mh_att), dcopy(pos_ff), dropout=dropout), num_layers ) return tranformer_encoder