utils.py

"""
Some codes are paraphrased from
http://jalammar.github.io/illustrated-transformer/
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable

def clones(module, N):
    #Produce N identical layers.
    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])

# 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)))
        return x + self.dropout(sublayer(self.norm(x)))

    
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 attention(self, 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

    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 = 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(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 = 512, 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 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).float()
        div_term = torch.exp(torch.arange(0.0, 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)


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