import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import math, copy, time
import torch.nn.utils.rnn as rnn_utils
from data import get_cuda, to_var, calc_bleu
import numpy as np
def clones(module, N):
"""Produce N identical layers."""
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
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):
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 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 + 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
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 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)
################ Encoder ################
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 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)
################ Decoder ################
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)
################ Generator ################
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 AttentionScore(nn.Module):
"""
correlation_func = 1, sij = x1^Tx2
correlation_func = 2, sij = (Wx1)D(Wx2)
correlation_func = 3, sij = Relu(Wx1)DRelu(Wx2)
correlation_func = 4, sij = x1^TWx2
correlation_func = 5, sij = Relu(Wx1)DRelu(Wx2)
"""
def __init__(self, input_size, hidden_size, correlation_func=1, do_similarity=False):
super(AttentionScore, self).__init__()
self.correlation_func = correlation_func
self.hidden_size = hidden_size
if correlation_func == 2 or correlation_func == 3:
self.linear = nn.Linear(input_size, hidden_size, bias=False)
if do_similarity:
self.diagonal = Parameter(torch.ones(1, 1, 1) / (hidden_size ** 0.5), requires_grad=False)
else:
self.diagonal = Parameter(torch.ones(1, 1, hidden_size), requires_grad=True)
if correlation_func == 4:
self.linear = nn.Linear(input_size, input_size, bias=False)
if correlation_func == 5:
self.linear = nn.Linear(input_size, hidden_size, bias=False)
def forward(self, x1, x2, x2_mask):
'''
Input:
x1: batch x word_num1 x dim
x2: batch x word_num2 x dim
Output:
scores: batch x word_num1 x word_num2
'''
# x1 = dropout(x1, p = dropout_p, training = self.training)
# x2 = dropout(x2, p = dropout_p, training = self.training)
x1_rep = x1
x2_rep = x2
batch = x1_rep.size(0)
word_num1 = x1_rep.size(1)
word_num2 = x2_rep.size(1)
dim = x1_rep.size(2)
if self.correlation_func == 2 or self.correlation_func == 3:
x1_rep = self.linear(x1_rep.contiguous().view(-1, dim)).view(batch, word_num1, self.hidden_size) # Wx1
x2_rep = self.linear(x2_rep.contiguous().view(-1, dim)).view(batch, word_num2, self.hidden_size) # Wx2
if self.correlation_func == 3:
x1_rep = F.relu(x1_rep)
x2_rep = F.relu(x2_rep)
x1_rep = x1_rep * self.diagonal.expand_as(x1_rep)
# x1_rep is (Wx1)D or Relu(Wx1)D
# x1_rep: batch x word_num1 x dim (corr=1) or hidden_size (corr=2,3)
if self.correlation_func == 4:
x2_rep = self.linear(x2_rep.contiguous().view(-1, dim)).view(batch, word_num2, dim) # Wx2
if self.correlation_func == 5:
x1_rep = self.linear(x1_rep.contiguous().view(-1, dim)).view(batch, word_num1, self.hidden_size) # Wx1
x2_rep = self.linear(x2_rep.contiguous().view(-1, dim)).view(batch, word_num2, self.hidden_size) # Wx2
x1_rep = F.relu(x1_rep)
x2_rep = F.relu(x2_rep)
scores = x1_rep.bmm(x2_rep.transpose(1, 2))
empty_mask = x2_mask.eq(0).expand_as(scores)
scores.data.masked_fill_(empty_mask.data, -float('inf'))
# softmax
alpha_flat = F.softmax(scores, dim=-1)
return alpha_flat
class EncoderDecoder(nn.Module):
"""
A standard Encoder-Decoder architecture. Base for this and many other models.
"""
def __init__(self, encoder, decoder, gru, src_embed, tgt_embed, generator, input_size):
super(EncoderDecoder, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.gru = gru
self.src_embed = src_embed
self.tgt_embed = tgt_embed
self.generator = generator
self.linear = nn.Linear(input_size * 2, input_size)
self.attention = AttentionScore(input_size, input_size)
self.gru_decoder = nn.GRU(input_size * 2, input_size, 1)
def forward(self, src, tgt, src_mask, tgt_mask):
"""
Take in and process masked src and target sequences.
"""
memory = self.encode(src, src_mask) # (batch_size, max_src_seq, d_model)
# attented_mem=self.attention(memory,memory,memory,src_mask)
# memory=attented_mem
score = self.attention(memory, memory, src_mask)
attent_memory = score.bmm(memory)
# memory=self.linear(torch.cat([memory,attent_memory],dim=-1))
memory, _ = self.gru(attented_mem)
'''
score=torch.sigmoid(self.linear(memory))
memory=memory*score
'''
latent = torch.sum(memory, dim=1) # (batch_size, d_model)
logit = self.decode(latent.unsqueeze(1), tgt, tgt_mask) # (batch_size, max_tgt_seq, d_model)
# logit,_=self.gru_decoder(logit)
prob = self.generator(logit) # (batch_size, max_seq, vocab_size)
return latent, prob
def encode(self, src, src_mask):
return self.encoder(self.src_embed(src), src_mask)
def decode(self, memory, tgt, tgt_mask):
# memory: (batch_size, 1, d_model)
src_mask = get_cuda(torch.ones(memory.size(0), 1, 1).long())
# print("src_mask here", src_mask)
# print("src_mask", src_mask.size())
return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)
def greedy_decode(self, latent, max_len, start_id):
'''
latent: (batch_size, max_src_seq, d_model)
src_mask: (batch_size, 1, max_src_len)
'''
batch_size = latent.size(0)
ys = get_cuda(torch.ones(batch_size, 1).fill_(start_id).long()) # (batch_size, 1)
for i in range(max_len - 1):
# input("==========")
# print("="*10, i)
# print("ys", ys.size()) # (batch_size, i)
# print("tgt_mask", subsequent_mask(ys.size(1)).size()) # (1, i, i)
out = self.decode(latent.unsqueeze(1), to_var(ys), to_var(subsequent_mask(ys.size(1)).long()))
prob = self.generator(out[:, -1])
# print("prob", prob.size()) # (batch_size, vocab_size)
_, next_word = torch.max(prob, dim=1)
# print("next_word", next_word.size()) # (batch_size)
# print("next_word.unsqueeze(1)", next_word.unsqueeze(1).size())
ys = torch.cat([ys, next_word.unsqueeze(1)], dim=1)
# print("ys", ys.size())
return ys[:, 1:]
def make_model(d_vocab, N, d_model, d_ff=1024, h=4, 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), N),
Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N),
nn.GRU(d_model, d_model, 1),
nn.Sequential(Embeddings(d_model, d_vocab), c(position)),
nn.Sequential(Embeddings(d_model, d_vocab), c(position)),
Generator(d_model, d_vocab),
d_model
)
# 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 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
class Batch:
"""Object for holding a batch of data with mask during training."""
def __init__(self, src, trg=None, pad=0):
self.src = src
self.src_mask = (src != pad).unsqueeze(-2)
if trg is not None:
self.trg = trg[:, :-1]
self.trg_y = trg[:, 1:]
self.trg_mask = self.make_std_mask(self.trg, pad)
self.ntokens = (self.trg_y != pad).data.sum()
@staticmethod
def make_std_mask(tgt, pad):
"""Create a mask to hide padding and future words."""
tgt_mask = (tgt != pad).unsqueeze(-2)
tgt_mask = tgt_mask & Variable(
subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))
return tgt_mask
class NoamOpt:
"Optim wrapper that implements rate."
def __init__(self, model_size, factor, warmup, optimizer):
self.optimizer = optimizer
self._step = 0
self.warmup = warmup
self.factor = factor
self.model_size = model_size
self._rate = 0
def step(self):
"Update parameters and rate"
self._step += 1
rate = self.rate()
for p in self.optimizer.param_groups:
p['lr'] = rate
self._rate = rate
self.optimizer.step()
def rate(self, step=None):
"Implement `lrate` above"
if step is None:
step = self._step
return self.factor * \
(self.model_size ** (-0.5) *
min(step ** (-0.5), step * self.warmup ** (-1.5)))
def get_std_opt(model):
return NoamOpt(model.src_embed[0].d_model, 2, 4000,
torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
class LabelSmoothing(nn.Module):
"""Implement label smoothing."""
def __init__(self, size, padding_idx, smoothing=0.0):
super(LabelSmoothing, self).__init__()
self.criterion = nn.KLDivLoss(size_average=False)
self.padding_idx = padding_idx
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
self.size = size
self.true_dist = None
def forward(self, x, target):
assert x.size(1) == self.size
true_dist = x.data.clone()
true_dist.fill_(self.smoothing / (self.size - 2))
true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
true_dist[:, self.padding_idx] = 0
mask = torch.nonzero(target.data == self.padding_idx)
if mask.dim() > 0:
true_dist.index_fill_(0, mask.squeeze(), 0.0)
self.true_dist = true_dist
return self.criterion(x, Variable(true_dist, requires_grad=False))
class Classifier(nn.Module):
def __init__(self, latent_size, output_size):
super().__init__()
self.fc1 = nn.Linear(latent_size, 100)
self.relu1 = nn.LeakyReLU(0.2, )
self.fc2 = nn.Linear(100, 50)
self.relu2 = nn.LeakyReLU(0.2)
self.fc3 = nn.Linear(50, output_size)
self.sigmoid = nn.Sigmoid()
def forward(self, input):
out = self.fc1(input)
out = self.relu1(out)
out = self.fc2(out)
out = self.relu2(out)
out = self.fc3(out)
out = self.sigmoid(out)
# out = F.log_softmax(out, dim=1)
return out # batch_size * label_size
def fgim_attack(model, origin_data, target, ae_model, max_sequence_length, id_bos,
id2text_sentence, id_to_word, gold_ans):
"""Fast Gradient Iterative Methods"""
dis_criterion = nn.BCELoss(size_average=True)
gold_text = id2text_sentence(gold_ans, id_to_word)
print("gold:", gold_text)
# while True:
for epsilon in [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]:
it = 0
data = origin_data
while True:
print("epsilon:", epsilon)
data = to_var(data.clone()) # (batch_size, seq_length, latent_size)
# Set requires_grad attribute of tensor. Important for Attack
data.requires_grad = True
output = model.forward(data)
# Calculate gradients of model in backward pass
# print("target", target[0].item())
# print("output", output[0].item())
loss = dis_criterion(output, target)
model.zero_grad()
loss.backward()
data_grad = data.grad.data
# print("data_grad")
# print(data_grad)
data = data - epsilon * data_grad
# print("epsilon * data_grad")
# print((epsilon * data_grad))
# print("data")
# print(data)
# print("perturbed_data")
# print(perturbed_data)
it += 1
# data = perturbed_data
epsilon = epsilon * 0.9
generator_id = ae_model.greedy_decode(data,
max_len=max_sequence_length,
start_id=id_bos)
generator_text = id2text_sentence(generator_id[0], id_to_word)
print("| It {:2d} | dis model pred {:5.4f} |".format(it, output[0].item()))
print(generator_text)
if it >= 5:
break
return
if __name__ == '__main__':
# plt.figure(figsize=(15, 5))
# pe = PositionalEncoding(20, 0)
# y = pe.forward(Variable(torch.zeros(1, 100, 20)))
# plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())
# plt.legend(["dim %d" % p for p in [4, 5, 6, 7]])
# plt.show()
# Small example model.
# tmp_model = make_model(10, 10, 2)
pass
