You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
133 lines
4.1 KiB
133 lines
4.1 KiB
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.autograd import Variable
|
|
import numpy as np
|
|
import math
|
|
import os
|
|
import copy
|
|
|
|
def clones(module, N):
|
|
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
|
|
|
|
class Encoder(nn.Module):
|
|
def __init__(self, layer, N):
|
|
super(Encoder, self).__init__()
|
|
self.layers = clones(layer, N)
|
|
self.norm = LayerNorm(layer.size)
|
|
|
|
def forward(self, x, mask):
|
|
for layer in self.layers:
|
|
x = layer(x, mask)
|
|
return x
|
|
|
|
class LayerNorm(nn.Module):
|
|
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
|
|
|
|
def attention(query, key, value, mask=None, dropout=None):
|
|
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 SublayerConnection(nn.Module):
|
|
def __init__(self, size, dropout):
|
|
super(SublayerConnection, self).__init__()
|
|
self.norm = LayerNorm(size)
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
def forward(self, x, sublayer):
|
|
return x + self.dropout(sublayer(self.norm(x)))
|
|
|
|
|
|
class EncoderLayer(nn.Module):
|
|
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):
|
|
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
|
|
return self.sublayer[1](x, self.feed_forward)
|
|
|
|
|
|
class MultiHeadedAttention(nn.Module):
|
|
def __init__(self, h, d_model, dropout=0.1):
|
|
super(MultiHeadedAttention, self).__init__()
|
|
assert d_model % h == 0
|
|
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):
|
|
if mask is not None:
|
|
mask = mask.unsqueeze(1)
|
|
nbatches = query.size(0)
|
|
|
|
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))]
|
|
|
|
x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
|
|
|
|
x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
|
|
return self.linears[-1](x)
|
|
|
|
|
|
class PositionwiseFeedForward(nn.Module):
|
|
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.gelu = nn.ReLU()
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
def forward(self, x):
|
|
return self.w_2(self.dropout(self.gelu(self.w_1(x))))
|
|
|
|
class Transformer(nn.Module):
|
|
def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27):
|
|
super(Transformer, self).__init__()
|
|
|
|
self.pos_embedding = nn.Parameter(torch.randn(1, length, d_model))
|
|
self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout)
|
|
|
|
def forward(self, x, mask=None):
|
|
|
|
x += self.pos_embedding
|
|
|
|
x = self.model(x, mask)
|
|
|
|
return x
|
|
|
|
def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1):
|
|
c = copy.deepcopy
|
|
attn = MultiHeadedAttention(h, d_model)
|
|
ff = PositionwiseFeedForward(d_model, d_ff, dropout)
|
|
model = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N)
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|