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VAE_Text_Generation.py
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VAE_Text_Generation.py
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""" VAE for Text Generation
This is for Module 1: Candidates Generation.
Usage: python VAE_Text_Generation.py --dataset reddit
"""
import argparse
import math
import os
import numpy as np
import torch as T
import torch.nn.functional as F
from tqdm import tqdm
from utility.VAE_Text_Generation.dataset import get_iterators
from utility.VAE_Text_Generation.helper_functions import get_cuda
from utility.VAE_Text_Generation.model import VAE
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=8)
parser.add_argument('--n_vocab', type=int, default=12000)
parser.add_argument('--epochs', type=int, default=1000)
parser.add_argument('--n_hidden_G', type=int, default=512)
parser.add_argument('--n_layers_G', type=int, default=2)
parser.add_argument('--n_hidden_E', type=int, default=512)
parser.add_argument('--n_layers_E', type=int, default=1)
parser.add_argument('--n_z', type=int, default=100)
parser.add_argument('--word_dropout', type=float, default=0.5)
parser.add_argument('--rec_coef', type=float, default=7)
parser.add_argument('--lr', type=float, default=0.00001)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--n_highway_layers', type=int, default=2)
parser.add_argument('--n_embed', type=int, default=300)
parser.add_argument('--out_num', type=int, default=30000)
parser.add_argument('--unk_token', type=str, default="<unk>")
parser.add_argument('--pad_token', type=str, default="<pad>")
parser.add_argument('--start_token', type=str, default="<sos>")
parser.add_argument('--end_token', type=str, default="<eos>")
parser.add_argument('--dataset', type=str, default="reddit")
parser.add_argument('--training', action='store_true')
parser.add_argument('--resume_training', action='store_true')
opt = parser.parse_args()
print(opt)
save_path = "tmp/saved_VAE_models/" + opt.dataset + ".tar"
print(save_path)
if not os.path.exists("tmp/saved_VAE_models"):
os.makedirs("tmp/saved_VAE_models")
os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu)
candidates_path = opt.dataset + '_for_VAE.txt'
train_iter, val_iter, vocab = get_iterators(opt, path='./data/', fname=candidates_path)
opt.n_vocab = len(vocab)
if opt.training:
vae = VAE(opt)
vae.embedding.weight.data.copy_(vocab.vectors) #Intialize trainable embeddings with pretrained glove vectors
vae = get_cuda(vae)
trainer_vae = T.optim.Adam(vae.parameters(), lr=opt.lr)
else:
checkpoint = T.load(save_path)
vae = checkpoint['vae_dict']
trainer_vae = checkpoint['vae_trainer']
if 'opt' in checkpoint:
opt_old = checkpoint['opt']
print(opt_old)
def create_generator_input(x, train):
G_inp = x[:, 0:x.size(1)-1].clone() #input for generator should exclude last word of sequence
if train == False:
return G_inp
r = np.random.rand(G_inp.size(0), G_inp.size(1)) #Perform word_dropout according to random values (r) generated for each word
for i in range(len(G_inp)):
for j in range(1, G_inp.size(1)):
if r[i, j] < opt.word_dropout and G_inp[i, j] not in [vocab.stoi[opt.pad_token], vocab.stoi[opt.end_token]]:
G_inp[i, j] = vocab.stoi[opt.unk_token]
return G_inp
def train_batch(x, G_inp, step, train=True):
logit, _, kld = vae(x, G_inp, None, None)
logit = logit.view(-1, opt.n_vocab) #converting into shape (batch_size*(n_seq-1), n_vocab) to facilitate performing F.cross_entropy()
x = x[:, 1:x.size(1)] #target for generator should exclude first word of sequence
x = x.contiguous().view(-1) #converting into shape (batch_size*(n_seq-1),1) to facilitate performing F.cross_entropy()
rec_loss = F.cross_entropy(logit, x)
kld_coef = (math.tanh((step - 15000)/1000) + 1) / 2
# kld_coef = min(1,step/(200000.0))
loss = opt.rec_coef*rec_loss + kld_coef*kld
if train==True: #skip below step if we are performing validation
trainer_vae.zero_grad()
loss.backward()
trainer_vae.step()
return rec_loss.item(), kld.item()
# def load_model_from_checkpoint():
# global vae, trainer_vae
# checkpoint = T.load(save_path)
# vae.load_state_dict(checkpoint['vae_dict'])
# trainer_vae.load_state_dict(checkpoint['vae_trainer'])
# return checkpoint['step'], checkpoint['epoch']
def training():
start_epoch = step = 0
if opt.resume_training:
step, start_epoch = checkpoint['step'], checkpoint['epoch']
for epoch in range(start_epoch, opt.epochs):
vae.train()
train_rec_loss = []
train_kl_loss = []
for batch in train_iter:
x = get_cuda(batch.text) #Used as encoder input as well as target output for generator
G_inp = create_generator_input(x, train=True)
rec_loss, kl_loss = train_batch(x, G_inp, step, train=True)
train_rec_loss.append(rec_loss)
train_kl_loss.append(kl_loss)
step += 1
vae.eval()
valid_rec_loss = []
valid_kl_loss = []
for batch in val_iter:
x = get_cuda(batch.text)
G_inp = create_generator_input(x, train=False)
with T.autograd.no_grad():
rec_loss, kl_loss = train_batch(x, G_inp, step, train=False)
valid_rec_loss.append(rec_loss)
valid_kl_loss.append(kl_loss)
train_rec_loss = np.mean(train_rec_loss)
train_kl_loss = np.mean(train_kl_loss)
valid_rec_loss = np.mean(valid_rec_loss)
valid_kl_loss = np.mean(valid_kl_loss)
print("No.", epoch, "T_rec:", '%.2f' % train_rec_loss, "T_kld:", '%.2f' % train_kl_loss, "V_rec:", '%.2f' % valid_rec_loss, "V_kld:", '%.2f' % valid_kl_loss)
if epoch >= 50 and epoch % 10 == 0:
print('save model ' + str(epoch) + '...')
T.save({'epoch': epoch + 1, 'vae_dict': vae, 'vae_trainer': trainer_vae, 'step': step, 'opt': opt}, save_path)
generate_sentences(5)
def generate_sentences(n_examples, save=0):
vae.eval()
out = []
for i in tqdm(range(n_examples)):
z = get_cuda(T.randn([1, vae.n_z]))
h_0 = get_cuda(T.zeros(vae.generator.n_layers_G, 1, vae.generator.n_hidden_G))
c_0 = get_cuda(T.zeros(vae.generator.n_layers_G, 1, vae.generator.n_hidden_G))
G_hidden = (h_0, c_0)
G_inp = T.LongTensor(1, 1).fill_(vocab.stoi[opt.start_token])
G_inp = get_cuda(G_inp)
out_str = ""
while (G_inp[0][0].item() != vocab.stoi[opt.end_token]) and (G_inp[0][0].item() != vocab.stoi[opt.pad_token]):
with T.autograd.no_grad():
logit, G_hidden, _ = vae(None, G_inp, z, G_hidden)
probs = F.softmax(logit[0], dim=1)
G_inp = T.multinomial(probs, 1)
out_str += (vocab.itos[G_inp[0][0].item()]+" ")
print(out_str[:-6])
out.append(out_str[:-6])
if save:
original = []
with open('./data/' + candidates_path, 'r') as fin:
for line in fin:
original.append(line.strip())
fname = './data/' + opt.dataset + '_candidates.txt'
with open(fname, 'w') as fout:
for i in out + original:
fout.write(i)
fout.write('\n')
if __name__ == '__main__':
if opt.training or opt.resume_training:
training()
generate_sentences(opt.out_num, save=1)
else:
generate_sentences(opt.out_num, save=1)