def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len,
cfg.padding_idx, cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim, cfg.vocab_size, cfg.padding_idx, gpu=cfg.CUDA)
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.BCEWithLogitsLoss()
# DataLoader
self.gen_data = GenDataIter(self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis))
self.dis_data = DisDataIter(self.gen_data.random_batch()['target'], self.train_data.random_batch()['target'])
# Metrics
self.bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target, self.index_word_dict),
real_text=tensor_to_tokens(self.test_data.target, self.test_data.index_word_dict), gram=3)
self.self_bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target, self.index_word_dict),
real_text=tensor_to_tokens(self.gen_data.target, self.index_word_dict),
gram=3)
def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim,
cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx,
cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.CrossEntropyLoss()
# DataLoader
self.gen_data = GenDataIter(
self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis))
def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim,
cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx,
cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
class LeakGANInstructor(BasicInstructor):
def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim,
cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx,
cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
def _run(self):
for inter_num in range(cfg.inter_epoch):
self.log.info('>>> Interleaved Round %d...' % inter_num)
self.sig.update() # update signal
if self.sig.pre_sig:
# ===DISCRIMINATOR PRE-TRAINING===
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.train_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(),
cfg.pretrained_dis_path)
print('Save pre-trained discriminator: {}'.format(
cfg.pretrained_dis_path))
# ===GENERATOR MLE TRAINING===
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(),
cfg.pretrained_gen_path)
print('Save pre-trained generator: {}'.format(
cfg.pretrained_gen_path))
else:
self.log.info(
'>>> Stop by pre_signal! Skip to adversarial training...')
break
# ===ADVERSARIAL TRAINING===
self.log.info('Starting Adversarial Training...')
self.log.info('Initial generator: %s' %
(str(self.cal_metrics(fmt_str=True))))
for adv_epoch in range(cfg.ADV_train_epoch):
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step) # Generator
self.train_discriminator(cfg.ADV_d_step, cfg.ADV_d_epoch,
'ADV') # Discriminator
if adv_epoch % cfg.adv_log_step == 0 or adv_epoch == cfg.ADV_train_epoch - 1:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info(
'>>> Stop by adv_signal! Finishing adversarial training...'
)
break
def _test(self):
print('>>> Begin test...')
self._run()
pass
def pretrain_generator(self, epochs):
"""
Max Likelihood Pretraining for the gen
- gen_opt: [mana_opt, work_opt]
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
pre_mana_loss = 0
pre_work_loss = 0
# ===Train===
for i, data in enumerate(self.oracle_data.loader):
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
mana_loss, work_loss = self.gen.pretrain_loss(
target, self.dis)
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
pre_mana_loss += mana_loss.data.item()
pre_work_loss += work_loss.data.item()
pre_mana_loss = pre_mana_loss / len(self.oracle_data.loader)
pre_work_loss = pre_work_loss / len(self.oracle_data.loader)
# ===Test===
if epoch % cfg.pre_log_step == 0 or epoch == epochs - 1:
self.log.info(
'[MLE-GEN] epoch %d : pre_mana_loss = %.4f, pre_work_loss = %.4f, %s'
% (epoch, pre_mana_loss, pre_work_loss,
self.cal_metrics(fmt_str=True)))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info(
'>>> Stop by pre signal, skip to adversarial training...')
break
def adv_train_generator(self, g_step, current_k=0):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
adv_mana_loss = 0
adv_work_loss = 0
for step in range(g_step):
with torch.no_grad():
gen_samples = self.gen.sample(
cfg.batch_size, cfg.batch_size, self.dis,
train=True) # !!! train=True, the only place
inp, target = GenDataIter.prepare(gen_samples, gpu=cfg.CUDA)
# ===Train===
rewards = rollout_func.get_reward_leakgan(
target, cfg.rollout_num, self.dis,
current_k).cpu() # reward with MC search
mana_loss, work_loss = self.gen.adversarial_loss(
target, rewards, self.dis)
# update parameters
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
adv_mana_loss += mana_loss.data.item()
adv_work_loss += work_loss.data.item()
# ===Test===
self.log.info(
'[ADV-GEN] adv_mana_loss = %.4f, adv_work_loss = %.4f, %s' %
(adv_mana_loss / g_step, adv_work_loss / g_step,
self.cal_metrics(fmt_str=True)))
def train_discriminator(self, d_step, d_epoch, phase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
# prepare loader for validate
global d_loss, train_acc
pos_val = self.oracle.sample(8 * cfg.batch_size, cfg.batch_size)
neg_val = self.gen.sample(8 * cfg.batch_size, cfg.batch_size, self.dis)
dis_eval_data = DisDataIter(pos_val, neg_val)
for step in range(d_step):
# prepare loader for training
pos_samples = self.oracle.sample(
cfg.samples_num, cfg.batch_size) # re-sample the Oracle Data
neg_samples = self.gen.sample(cfg.samples_num, cfg.batch_size,
self.dis)
dis_data = DisDataIter(pos_samples, neg_samples)
for epoch in range(d_epoch):
# ===Train===
d_loss, train_acc = self.train_dis_epoch(
self.dis, dis_data.loader, self.dis_criterion,
self.dis_opt)
# ===Test===
_, eval_acc = self.eval_dis(self.dis, dis_eval_data.loader,
self.dis_criterion)
self.log.info(
'[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f, eval_acc = %.4f,'
% (phase, step, d_loss, train_acc, eval_acc))
def cal_metrics(self, fmt_str=False):
# Prepare data for evaluation
gen_data = GenDataIter(
self.gen.sample(cfg.samples_num, cfg.batch_size, self.dis))
# Reset metrics
self.nll_oracle.reset(self.oracle, gen_data.loader)
self.nll_gen.reset(self.gen,
self.oracle_data.loader,
leak_dis=self.dis)
self.nll_div.reset(self.gen, gen_data.loader, leak_dis=self.dis)
if fmt_str:
return ', '.join([
'%s = %s' % (metric.get_name(), metric.get_score())
for metric in self.all_metrics
])
else:
return [metric.get_score() for metric in self.all_metrics]
def _save(self, phase, epoch):
torch.save(
self.gen.state_dict(),
cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_{}_{:05d}.txt'.format(
phase, epoch)
samples = self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis)
write_tensor(save_sample_path, samples)
class LeakGANInstructor(BasicInstructor):
def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim,
cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx,
cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.CrossEntropyLoss()
# DataLoader
self.gen_data = GenDataIter(
self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis))
self.dis_data = DisDataIter(self.gen_data.random_batch()['target'],
self.train_data.random_batch()['target'])
# Metrics
self.bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(
self.test_data.target,
self.test_data.index_word_dict),
gram=3)
self.self_bleu = BLEU(
test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
gram=3)
def _run(self):
for inter_num in range(cfg.inter_epoch):
self.log.info('>>> Interleaved Round %d...' % inter_num)
self.sig.update() # update signal
if self.sig.pre_sig:
# =====DISCRIMINATOR PRE-TRAINING=====
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.train_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(),
cfg.pretrained_dis_path)
print('Save pre-trained discriminator: {}'.format(
cfg.pretrained_dis_path))
# =====GENERATOR MLE TRAINING=====
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(),
cfg.pretrained_gen_path)
print('Save pre-trained generator: {}'.format(
cfg.pretrained_gen_path))
else:
self.log.info(
'>>> Stop by pre_signal! Skip to adversarial training...')
break
# =====ADVERSARIAL TRAINING=====
self.log.info('Starting Adversarial Training...')
self.log.info('Initial generator: %s' %
(str(self.cal_metrics(fmt_str=True))))
for adv_epoch in range(cfg.ADV_train_epoch):
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step) # Generator
self.train_discriminator(cfg.ADV_d_step, cfg.ADV_d_epoch,
'ADV') # Discriminator
if adv_epoch % cfg.adv_log_step == 0:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info(
'>>> Stop by adv_signal! Finishing adversarial training...'
)
break
def _test(self):
print('>>> Begin test...')
self._run()
pass
def pretrain_generator(self, epochs):
"""
Max Likelihood Pretraining for the gen
- gen_opt: [mana_opt, work_opt]
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
pre_mana_loss = 0
pre_work_loss = 0
# =====Train=====
for i, data in enumerate(self.train_data.loader):
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
mana_loss, work_loss = self.gen.pretrain_loss(
target, self.dis)
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
pre_mana_loss += mana_loss.data.item()
pre_work_loss += work_loss.data.item()
pre_mana_loss = pre_mana_loss / len(self.train_data.loader)
pre_work_loss = pre_work_loss / len(self.train_data.loader)
# =====Test=====
if epoch % cfg.pre_log_step == 0:
self.log.info(
'[MLE-GEN] epoch %d : pre_mana_loss = %.4f, pre_work_loss = %.4f, %s'
% (epoch, pre_mana_loss, pre_work_loss,
self.cal_metrics(fmt_str=True)))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info(
'>>> Stop by pre signal, skip to adversarial training...')
break
def adv_train_generator(self, g_step, current_k=0):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
adv_mana_loss = 0
adv_work_loss = 0
for step in range(g_step):
with torch.no_grad():
gen_samples = self.gen.sample(
cfg.batch_size, cfg.batch_size, self.dis,
train=True) # !!! train=True, the only place
inp, target = self.gen_data.prepare(gen_samples, gpu=cfg.CUDA)
# =====Train=====
rewards = rollout_func.get_reward_leakgan(
target, cfg.rollout_num, self.dis,
current_k).cpu() # reward with MC search
mana_loss, work_loss = self.gen.adversarial_loss(
target, rewards, self.dis)
# update parameters
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
adv_mana_loss += mana_loss.data.item()
adv_work_loss += work_loss.data.item()
# =====Test=====
self.log.info(
'[ADV-GEN] adv_mana_loss = %.4f, adv_work_loss = %.4f, %s' %
(adv_mana_loss / g_step, adv_work_loss / g_step,
self.cal_metrics(fmt_str=True)))
def train_discriminator(self, d_step, d_epoch, phrase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
for step in range(d_step):
# prepare loader for training
pos_samples = self.train_data.target
neg_samples = self.gen.sample(cfg.samples_num, cfg.batch_size,
self.dis)
self.dis_data.reset(pos_samples, neg_samples)
for epoch in range(d_epoch):
# =====Train=====
d_loss, train_acc = self.train_dis_epoch(
self.dis, self.dis_data.loader, self.dis_criterion,
self.dis_opt)
# =====Test=====
self.log.info(
'[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f,' %
(phrase, step, d_loss, train_acc))
def cal_metrics(self, fmt_str=False):
self.gen_data.reset(
self.gen.sample(cfg.samples_num, cfg.batch_size, self.dis))
self.bleu.test_text = tensor_to_tokens(self.gen_data.target,
self.index_word_dict)
bleu_score = self.bleu.get_score(ignore=False)
with torch.no_grad():
gen_nll = 0
for data in self.train_data.loader:
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
loss = self.gen.batchNLLLoss(target, self.dis)
gen_nll += loss.item()
gen_nll /= len(self.train_data.loader)
if fmt_str:
'''
print('bleu_score:\n')
print(bleu_score)
print('gen_nll:\n')
print(gen_nll)
'''
return 'BLEU-3 = %.4f, gen_NLL = %.4f,' % (bleu_score[0], gen_nll)
return bleu_score, gen_nll
def _save(self, phrase, epoch):
torch.save(
self.gen.state_dict(),
cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phrase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_{}_{:05d}.txt'.format(
phrase, epoch)
samples = self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis)
write_tokens(save_sample_path,
tensor_to_tokens(samples, self.index_word_dict))